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What is Observational Study Design and Types

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Most people think of a traditional experimental design when they consider research and published research papers. There is, however, a type of research that is more observational in nature, and it is appropriately referred to as “observational studies.”

There are many valuable reasons to utilize an observational study design. But, just as in research experimental design, different methods can be used when you’re considering this type of study. In this article, we’ll look at the advantages and disadvantages of an observational study design, as well as the 3 types of observational studies.

What is Observational Study Design?

An observational study is when researchers are looking at the effect of some type of intervention, risk, a diagnostic test or treatment, without trying to manipulate who is, or who isn’t, exposed to it.

This differs from an experimental study, where the scientists are manipulating who is exposed to the treatment, intervention, etc., by having a control group, or those who are not exposed, and an experimental group, or those who are exposed to the intervention, treatment, etc. In the best studies, the groups are randomized, or chosen by chance.

Any evidence derived from systematic reviews is considered the best in the hierarchy of evidence, which considers which studies are deemed the most reliable. Next would be any evidence that comes from randomized controlled trials. Cohort studies and case studies follow, in that order.

Cohort studies and case studies are considered observational in design, whereas the randomized controlled trial would be an experimental study.

Let’s take a closer look at the different types of observational study design.

The 3 types of Observational Studies

The different types of observational studies are used for different reasons. Selecting the best type for your research is critical to a successful outcome. One of the main reasons observational studies are used is when a randomized experiment would be considered unethical. For example, a life-saving medication used in a public health emergency. They are also used when looking at aetiology, or the cause of a condition or disease, as well as the treatment of rare conditions.

Case Control Observational Study

Researchers in case control studies identify individuals with an existing health issue or condition, or “cases,” along with a similar group without the condition, or “controls.” These two groups are then compared to identify predictors and outcomes. This type of study is helpful to generate a hypothesis that can then be researched.

Cohort Observational Study

This type of observational study is often used to help understand cause and effect. A cohort observational study looks at causes, incidence and prognosis, for example. A cohort is a group of people who are linked in a particular way, for example, a birth cohort would include people who were born within a specific period of time. Scientists might compare what happens to the members of the cohort who have been exposed to some variable to what occurs with members of the cohort who haven’t been exposed.

Cross Sectional Observational Study

Unlike a cohort observational study, a cross sectional observational study does not explore cause and effect, but instead looks at prevalence. Here you would look at data from a particular group at one very specific period of time. Researchers would simply observe and record information about something present in the population, without manipulating any variables or interventions. These types of studies are commonly used in psychology, education and social science.

Advantages and Disadvantages of Observational Study Design

Observational study designs have the distinct advantage of allowing researchers to explore answers to questions where a randomized controlled trial, or RCT, would be unethical. Additionally, if the study is focused on a rare condition, studying existing cases as compared to non-affected individuals might be the most effective way to identify possible causes of the condition. Likewise, if very little is known about a condition or circumstance, a cohort study would be a good study design choice.

A primary advantage to the observational study design is that they can generally be completed quickly and inexpensively. A RCT can take years before the data is compiled and available. RCTs are more complex and involved, requiring many more logistics and details to iron out, whereas an observational study can be more easily designed and completed.

The main disadvantage of observational study designs is that they’re more open to dispute than an RCT. Of particular concern would be confounding biases. This is when a cohort might share other characteristics that affect the outcome versus the outcome stated in the study. An example would be that people who practice good sleeping habits have less heart disease. But, maybe those who practice effective sleeping habits also, in general, eat better and exercise more.

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What is an Observational Study: Definition & Examples

By Jim Frost 10 Comments

What is an Observational Study?

An observational study uses sample data to find correlations in situations where the researchers do not control the treatment, or independent variable, that relates to the primary research question. The definition of an observational study hinges on the notion that the researchers only observe subjects and do not assign them to the control and treatment groups. That’s the key difference between an observational study vs experiment. These studies are also known as quasi-experiments and correlational studies .

True experiments assign subject to the experimental groups where the researchers can manipulate the conditions. Unfortunately, random assignment is not always possible. For these cases, you can conduct an observational study.

In this post, learn about the types of observational studies, why they are susceptible to confounding variables, and how they compare to experiments. I’ll close this post by reviewing a published observational study about vitamin supplement usage.

Observational Study Definition

In an observational study, the researchers only observe the subjects and do not interfere or try to influence the outcomes. In other words, the researchers do not control the treatments or assign subjects to experimental groups. Instead, they observe and measure variables of interest and look for relationships between them. Usually, researchers conduct observational studies when it is difficult, impossible, or unethical to assign study participants to the experimental groups randomly. If you can’t randomly assign subjects to the treatment and control groups, then you observe the subjects in their self-selected states.

Observational Study vs Experiment

Randomized experiments provide better results than observational studies. Consequently, you should always use a randomized experiment whenever possible. However, if randomization is not possible, science should not come to a halt. After all, we still want to learn things, discover relationships, and make discoveries. For these cases, observational studies are a good alternative to a true experiment. Let’s compare the differences between an observational study vs. an experiment.

Random assignment in an experiment reduces systematic differences between experimental groups at the beginning of the study, which increases your confidence that the treatments caused any differences between groups you observe at the end of the study. In contrast, an observational study uses self-formed groups that can have pre-existing differences, which introduces the problem of confounding variables. More on that later!

In a randomized experiment, randomization tends to equalize confounders between groups and, thereby, prevents problems. In my post about random assignment , I describe that process as an elegant solution for confounding variables. You don’t need to measure or even know which variables are confounders, and randomization will still mitigate their effects. Additionally, you can use control variables in an experiment to keep the conditions as consistent as possible. For more detail about the differences, read Observational Study vs. Experiment .

If you’re looking for a middle ground choice between observational studies vs experiments, consider using a quasi-experimental design. These methods don’t require you to randomly assign participants to the experimental groups and still allow you to draw better causal conclusions about an intervention than an observational study. Learn more about Quasi-Experimental Design Overview & Examples .

Related posts : Experimental Design: Definition and Examples , Randomized Controlled Trials (RCTs) , and Control Groups in Experiments

Observational Study Examples

Consider using an observational study when random assignment for an experiment is problematic. This approach allows us to proceed and draw conclusions about effects even though we can’t control the independent variables. The following observational study examples will help you understand when and why to use them.

For example, if you’re studying how depression affects performance of an activity, it’s impossible to assign subjects to the depression and control group randomly. However, you can have subjects with and without depression perform the activity and compare the results in an observational study.

Or imagine trying to assign subjects to cigarette smoking and non-smoking groups randomly?! However, you can observe people in both groups and assess the differences in health outcomes in an observational study.

Suppose you’re studying a treatment for a disease. Ideally, you recruit a group of patients who all have the disease, and then randomly assign them to the treatment and control group. However, it’s unethical to withhold the treatment, which rules out a control group. Instead, you can compare patients who voluntarily do not use the medicine to those who do use it.

In all these observational study examples, the researchers do not assign subjects to the experimental groups. Instead, they observe people who are already in these groups and compare the outcomes. Hence, the scientists must use an observational study vs. an experiment.

Types of Observational Studies

The observational study definition states that researchers only observe the outcomes and do not manipulate or control factors . Despite this limitation, there various types of observational studies.

The following experimental designs are three standard types of observational studies.

• Cohort Study : A longitudinal observational study that follows a group who share a defining characteristic. These studies frequently determine whether exposure to risk factor affects an outcome over time.
• Case-Control Study : A retrospective observational study that compares two existing groups—the case group with the condition and the control group without it. Researchers compare the groups looking for potential risk factors for the condition.
• Cross-Sectional Study : Takes a snapshot of a moment in time so researchers can understand the prevalence of outcomes and correlations between variables at that instant.

Qualitative research studies are usually observational in nature, but they collect non-numeric data and do not perform statistical analyses.

Retrospective studies must be observational.

Later in this post, we’ll closely examine a quantitative observational study example that assesses vitamin supplement consumption and how that affects the risk of death. It’s possible to use random assignment to place each subject in either the vitamin treatment group or the control group. However, the study assesses vitamin consumption in 40,000 participants over the course of two decades. It’s unrealistic to enforce the treatment and control protocols over such a long time for so many people!

Drawbacks of Observational Studies

While observational studies get around the inability to assign subjects randomly, this approach opens the door to the problem of confounding variables. A confounding variable, or confounder, correlates with both the experimental groups and the outcome variable. Because there is no random process that equalizes the experimental groups in an observational study, confounding variables can systematically differ between groups when the study begins. Consequently, confounders can be the actual cause for differences in outcome at the end of the study rather than the primary variable of interest. If an experiment does not account for confounding variables, confounders can bias the results and create spurious correlations .

Performing an observational study can decrease the internal validity of your study but increase the external validity. Learn more about internal and external validity .

Let’s see how this works. Imagine an observational study that compares people who take vitamin supplements to those who do not. People who use vitamin supplements voluntarily will tend to have other healthy habits that exist at the beginning of the study. These healthy habits are confounding variables. If there are differences in health outcomes at the end of the study, it’s possible that these healthy habits actually caused them rather than the vitamin consumption itself. In short, confounders confuse the results because they provide alternative explanations for the differences.

Despite the limitations, an observational study can be a valid approach. However, you must ensure that your research accounts for confounding variables. Fortunately, there are several methods for doing just that!

Learn more about Correlation vs. Causation: Understanding the Differences .

Accounting for Confounding Variables in an Observational Study

Because observational studies don’t use random assignment, confounders can be distributed disproportionately between conditions. Consequently, experimenters need to know which variables are confounders, measure them, and then use a method to account for them. It involves more work, and the additional measurements can increase the costs. And there’s always a chance that researchers will fail to identify a confounder, not account for it, and produce biased results. However, if randomization isn’t an option, then you probably need to consider an observational study.

Trait matching and statistically controlling confounders using multivariate procedures are two standard approaches for incorporating confounding variables.

Related post : Causation versus Correlation in Statistics

Matching in Observational Studies

Matching is a technique that involves selecting study participants with similar characteristics outside the variable of interest or treatment. Rather than using random assignment to equalize the experimental groups, the experimenters do it by matching observable characteristics. For every participant in the treatment group, the researchers find a participant with comparable traits to include in the control group. Matching subjects facilitates valid comparisons between those groups. The researchers use subject-area knowledge to identify characteristics that are critical to match.

For example, a vitamin supplement study using matching will select subjects who have similar health-related habits and attributes. The goal is that vitamin consumption will be the primary difference between the groups, which helps you attribute differences in health outcomes to vitamin consumption. However, the researchers are still observing participants who decide whether they consume supplements.

Matching has some drawbacks. The experimenters might not be aware of all the relevant characteristics they need to match. In other words, the groups might be different in an essential aspect that the researchers don’t recognize. For example, in the hypothetical vitamin study, there might be a healthy habit or attribute that affects the outcome that the researchers don’t measure and match. These unmatched characteristics might cause the observed differences in outcomes rather than vitamin consumption.

Learn more about Matched Pairs Design: Uses & Examples .

Using Multiple Regression in Observational Studies

Random assignment and matching use different methods to equalize the experimental groups in an observational study. However, statistical techniques, such as multiple regression analysis , don’t try to equalize the groups but instead use a model that accounts for confounding variables. These studies statistically control for confounding variables.

In multiple regression analysis, including a variable in the model holds it constant while you vary the variable/treatment of interest. For information about this property, read my post When Should I Use Regression Analysis?

As with matching, the challenge is to identify, measure, and include all confounders in the regression model. Failure to include a confounding variable in a regression model can cause omitted variable bias to distort your results.

Next, we’ll look at a published observational study that uses multiple regression to account for confounding variables.

Related post : Independent and Dependent Variables in a Regression Model

Vitamin Supplement Observational Study Example

Murso et al. (2011)* use a longitudinal observational study that ran 22 years to assess differences in death rates for subjects who used vitamin supplements regularly compared to those who did not use them. This study used surveys to record the characteristics of approximately 40,000 participants. The surveys asked questions about potential confounding variables such as demographic information, food intake, health details, physical activity, and, of course, supplement intake.

Because this is an observational study, the subjects decided for themselves whether they were taking vitamin supplements. Consequently, it’s safe to assume that supplement users and non-users might be different in other ways. From their article, the researchers found the following pre-existing differences between the two groups:

Supplement users had a lower prevalence of diabetes mellitus, high blood pressure, and smoking status; a lower BMI and waist to hip ratio, and were less likely to live on a farm. Supplement users had a higher educational level, were more physically active and were more likely to use estrogen replacement therapy. Also, supplement users were more likely to have a lower intake of energy, total fat, and monounsaturated fatty acids, saturated fatty acids and to have a higher intake of protein, carbohydrates, polyunsaturated fatty acids, alcohol, whole grain products, fruits, and vegetables.

Whew! That’s a long list of differences! Supplement users were different from non-users in a multitude of ways that are likely to affect their risk of dying. The researchers must account for these confounding variables when they compare supplement users to non-users. If they do not, their results can be biased.

This example illustrates a key difference between an observational study vs experiment. In a randomized experiment, the randomization would have equalized the characteristics of those the researchers assigned to the treatment and control groups. Instead, the study works with self-sorted groups that have numerous pre-existing differences!

Using Multiple Regression to Statistically Control for Confounders

To account for these initial differences in the vitamin supplement observational study, the researchers use regression analysis and include the confounding variables in the model.

The researchers present three regression models. The simplest model accounts only for age and caloric intake. Next, are two models that include additional confounding variables beyond age and calories. The first model adds various demographic information and seven health measures. The second model includes everything in the previous model and adds several more specific dietary intake measures. Using statistical significance as a guide for specifying the correct regression model , the researchers present the model with the most variables as the basis for their final results.

It’s instructive to compare the raw results and the final regression results.

Raw results

The raw differences in death risks for consumers of folic acid, vitamin B6, magnesium, zinc, copper, and multivitamins are NOT statistically significant. However, the raw results show a significant reduction in the death risk for users of B complex, C, calcium, D, and E.

However, those are the raw results for the observational study, and they do not control for the long list of differences between the groups that exist at the beginning of the study. After using the regression model to control for the confounding variables statistically, the results change dramatically.

Adjusted results

Of the 15 supplements that the study tracked in the observational study, researchers found consuming seven of these supplements were linked to a statistically significant INCREASE in death risk ( p-value < 0.05): multivitamins (increase in death risk 2.4%), vitamin B6 (4.1%), iron (3.9%), folic acid (5.9%), zinc (3.0%), magnesium (3.6%), and copper (18.0%). Only calcium was associated with a statistically significant reduction in death risk of 3.8%.

In short, the raw results suggest that those who consume supplements either have the same or lower death risks than non-consumers. However, these results do not account for the multitude of healthier habits and attributes in the group that uses supplements.

In fact, these confounders seem to produce most of the apparent benefits in the raw results because, after you statistically control the effects of these confounding variables, the results worsen for those who consume vitamin supplements. The adjusted results indicate that most vitamin supplements actually increase your death risk!

This research illustrates the differences between an observational study vs experiment. Namely how the pre-existing differences between the groups allow confounders to bias the raw results, making the vitamin consumption outcomes look better than they really are.

In conclusion, if you can’t randomly assign subjects to the experimental groups, an observational study might be right for you. However, be aware that you’ll need to identify, measure, and account for confounding variables in your experimental design.

Jaakko Mursu, PhD; Kim Robien, PhD; Lisa J. Harnack, DrPH, MPH; Kyong Park, PhD; David R. Jacobs Jr, PhD; Dietary Supplements and Mortality Rate in Older Women: The Iowa Women’s Health Study ; Arch Intern Med . 2011;171(18):1625-1633.

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Reader Interactions

December 30, 2023 at 5:05 am

I see, but our professor required us to indicate what year it was put into the article. May you tell me what year was this published originally? <3

December 30, 2023 at 3:40 pm

December 29, 2023 at 10:46 am

Hi, may I use your article as a citation for my thesis paper? If so, may I know the exact date you published this article? Thank you!

December 29, 2023 at 2:13 pm

Definitely feel free to cite this article! 🙂

When citing online resources, you typically use an “Accessed” date rather than a publication date because online content can change over time. For more information, read Purdue University’s Citing Electronic Resources .

November 18, 2021 at 10:09 pm

Love your content and has been very helpful!

Can you please advise the question below using an observational data set:

I have three years of observational GPS data collected on athletes (2019/2020/2021). Approximately 14-15 athletes per game and 8 games per year. The GPS software outputs 50+ variables for each athlete in each game, which we have narrowed down to 16 variables of interest from previous research.

2 factors 1) Period (first half, second half, and whole game), 2) Position (two groups with three subgroups in each – forwards (group 1, group 2, group 3) and backs (group 1, group 2, group 3))

16 variables of interest – all numerical and scale variables. Some of these are correlated, but not all.

My understanding is that I can use a oneway ANOVA for each year on it’s own, using one factor at a time (period or position) with post hoc analysis. This is fine, if data meets assumptions and is normally distributed. This tells me any significant interactions between variables of interest with chosen factor. For example, with position factor, do forwards in group 1 cover more total running distance than forwards in group 2 or backs in group 3.

However, I want to go deeper with my analysis. If I want to see if forwards in group 1 cover more total running distance in period 1 than backs in group 3 in the same period, I need an additional factor and the oneway ANOVA does not suit. Therefore I can use a twoway ANOVA instead of 2 oneway ANOVA’s and that solves the issue, correct?

This is complicated further by looking to compare 2019 to 2020 or 2019 to 2021 to identify changes over time, which would introduce a third independent variable.

I believe this would require a threeway ANOVA for this observational data set. 3 factors – Position, Period, and Year?

Are there any issues or concerns you see at first glance?

I appreciate your time and consideration.

April 12, 2021 at 2:02 pm

Could an observational study use a correlational design.

e.g. measuring effects of two variables on happiness, if you’re not intervening.

April 13, 2021 at 12:14 am

Typically, with observational studies, you’d want to include potential confounders, etc. Consequently, I’ve seen regression analysis used more frequently for observational studies to be able to control for other things because you’re not using randomization. You could use correlation to observe the relationship. However, you wouldn’t be controlling for potential confounding variables. Just something to consider.

April 11, 2021 at 1:28 pm

Hi, If I am to administer moderate doses of coffee for a hypothetical experiment, does it raise ethical concerns? Can I use random assignment for it?

April 11, 2021 at 4:06 pm

I don’t see any inherent ethical problems here as long as you describe the participant’s experience in the experiment including the coffee consumption. They key with human subjects is “informed consent.” They’re agreeing to participate based on a full and accurate understanding of what participation involves. Additionally, you as a researcher, understand the process well enough to be able to ensure their safety.

In your study, as long as subject know they’ll be drinking coffee and agree to that, I don’t see a problem. It’s a proven safe substance for the vast majority of people. If potential subjects are aware of the need to consume coffee, they can determine whether they are ok with that before agreeing to participate.

June 17, 2019 at 4:51 am

Really great article which explains observational and experimental study very well. It presents broad picture with the case study which helped a lot in understanding the core concepts. Thanks

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Chapter 3:  Types of Studies in Clinical Research—Part I: Observational Studies

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Introduction, research design and studies.

• STUDIES OF RISK ASSESSMENT: OBSERVATIONAL STUDIES
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When you have completed this chapter, you will be able to understand:

Types of studies and their relation to the research objectives

The different types of studies

The difference between primary and secondary studies

The different types of primary studies

Descriptive and analytical observational studies

Various descriptive observational studies and their functions

Various analytical observational studies and their functions

The advantages and disadvantages of observational studies

The previous chapter described the various steps of planning and conducting a research study. This chapter briefly introduces the reader to the different types of studies and then elaborates on the observational studies. In observational studies, the researcher observes the involvement of the participants and collects data by simply observing events as they happen, without playing an active part in what takes place. In interventional, or experimental, studies, the investigator exposes the participants to some kind of intervention and tries to find a relation between the intervention and the outcome. Observational studies can be descriptive, like the case studies and case series, but are more commonly analytical (cross-sectional, case–control, and cohort studies). Descriptive observational studies describe characteristics of a population and usually do not have a hypothesis; they are sometimes hypothesis-generating studies. An analytical observational study, in addition, tries to find a causal relationship between two or more comparable groups (variables) and has a hypothesis to prove.

A study design is a road map or blueprint based on the type of research to be carried out. It starts with development of the research question, formulating a hypothesis and research objectives, and subsequent planning for carrying out the research. The research objectives of the proposed study determine the type of study to be undertaken.

Types of Studies

Type of studies in medical research can be broadly classified into primary and secondary studies. Primary studies are those that are actually performed by the investigators, while secondary studies summarize the results of different primary studies in the form of systematic reviews and meta-analyses without actually performing the studies. 1 Primary studies can be put into three groups based on the type of research undertaken: basic medical or experimental studies, epidemiologic studies, and clinical studies. Basic medical studies include research in animal experiments, cell studies, biochemical, genetic and physiologic investigations, and studies on the properties of drugs and materials. Epidemiologic studies investigate the distribution and historical changes in the frequency of diseases and the causes for these diseases, while clinical studies involve research in human subjects. However, it may be difficult to classify individual studies into one of these three main categories. 1 A more practical way to classify the types of research studies based on their function is to group them into observational and interventional (experimental) studies; the former can be further subclassified into descriptive and analytical studies ( Figure 3-1 ).

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Non-Experimental Research

32 Observational Research

Learning objectives.

• List the various types of observational research methods and distinguish between each.
• Describe the strengths and weakness of each observational research method. 

What Is Observational Research?

The term observational research is used to refer to several different types of non-experimental studies in which behavior is systematically observed and recorded. The goal of observational research is to describe a variable or set of variables. More generally, the goal is to obtain a snapshot of specific characteristics of an individual, group, or setting. As described previously, observational research is non-experimental because nothing is manipulated or controlled, and as such we cannot arrive at causal conclusions using this approach. The data that are collected in observational research studies are often qualitative in nature but they may also be quantitative or both (mixed-methods). There are several different types of observational methods that will be described below.

Naturalistic Observation

Naturalistic observation  is an observational method that involves observing people’s behavior in the environment in which it typically occurs. Thus naturalistic observation is a type of field research (as opposed to a type of laboratory research). Jane Goodall’s famous research on chimpanzees is a classic example of naturalistic observation. Dr.  Goodall spent three decades observing chimpanzees in their natural environment in East Africa. She examined such things as chimpanzee’s social structure, mating patterns, gender roles, family structure, and care of offspring by observing them in the wild. However, naturalistic observation  could more simply involve observing shoppers in a grocery store, children on a school playground, or psychiatric inpatients in their wards. Researchers engaged in naturalistic observation usually make their observations as unobtrusively as possible so that participants are not aware that they are being studied. Such an approach is called disguised naturalistic observation .  Ethically, this method is considered to be acceptable if the participants remain anonymous and the behavior occurs in a public setting where people would not normally have an expectation of privacy. Grocery shoppers putting items into their shopping carts, for example, are engaged in public behavior that is easily observable by store employees and other shoppers. For this reason, most researchers would consider it ethically acceptable to observe them for a study. On the other hand, one of the arguments against the ethicality of the naturalistic observation of “bathroom behavior” discussed earlier in the book is that people have a reasonable expectation of privacy even in a public restroom and that this expectation was violated. 

In cases where it is not ethical or practical to conduct disguised naturalistic observation, researchers can conduct  undisguised naturalistic observation where the participants are made aware of the researcher presence and monitoring of their behavior. However, one concern with undisguised naturalistic observation is  reactivity. Reactivity refers to when a measure changes participants’ behavior. In the case of undisguised naturalistic observation, the concern with reactivity is that when people know they are being observed and studied, they may act differently than they normally would. This type of reactivity is known as the Hawthorne effect . For instance, you may act much differently in a bar if you know that someone is observing you and recording your behaviors and this would invalidate the study. So disguised observation is less reactive and therefore can have higher validity because people are not aware that their behaviors are being observed and recorded. However, we now know that people often become used to being observed and with time they begin to behave naturally in the researcher’s presence. In other words, over time people habituate to being observed. Think about reality shows like Big Brother or Survivor where people are constantly being observed and recorded. While they may be on their best behavior at first, in a fairly short amount of time they are flirting, having sex, wearing next to nothing, screaming at each other, and occasionally behaving in ways that are embarrassing.

Participant Observation

Another approach to data collection in observational research is participant observation. In  participant observation , researchers become active participants in the group or situation they are studying. Participant observation is very similar to naturalistic observation in that it involves observing people’s behavior in the environment in which it typically occurs. As with naturalistic observation, the data that are collected can include interviews (usually unstructured), notes based on their observations and interactions, documents, photographs, and other artifacts. The only difference between naturalistic observation and participant observation is that researchers engaged in participant observation become active members of the group or situations they are studying. The basic rationale for participant observation is that there may be important information that is only accessible to, or can be interpreted only by, someone who is an active participant in the group or situation. Like naturalistic observation, participant observation can be either disguised or undisguised. In disguised participant observation , the researchers pretend to be members of the social group they are observing and conceal their true identity as researchers.

In a famous example of disguised participant observation, Leon Festinger and his colleagues infiltrated a doomsday cult known as the Seekers, whose members believed that the apocalypse would occur on December 21, 1954. Interested in studying how members of the group would cope psychologically when the prophecy inevitably failed, they carefully recorded the events and reactions of the cult members in the days before and after the supposed end of the world. Unsurprisingly, the cult members did not give up their belief but instead convinced themselves that it was their faith and efforts that saved the world from destruction. Festinger and his colleagues later published a book about this experience, which they used to illustrate the theory of cognitive dissonance (Festinger, Riecken, & Schachter, 1956) [1] .

In contrast with undisguised participant observation ,  the researchers become a part of the group they are studying and they disclose their true identity as researchers to the group under investigation. Once again there are important ethical issues to consider with disguised participant observation.  First no informed consent can be obtained and second deception is being used. The researcher is deceiving the participants by intentionally withholding information about their motivations for being a part of the social group they are studying. But sometimes disguised participation is the only way to access a protective group (like a cult). Further, disguised participant observation is less prone to reactivity than undisguised participant observation. 

Rosenhan’s study (1973) [2]   of the experience of people in a psychiatric ward would be considered disguised participant observation because Rosenhan and his pseudopatients were admitted into psychiatric hospitals on the pretense of being patients so that they could observe the way that psychiatric patients are treated by staff. The staff and other patients were unaware of their true identities as researchers.

Another example of participant observation comes from a study by sociologist Amy Wilkins on a university-based religious organization that emphasized how happy its members were (Wilkins, 2008) [3] . Wilkins spent 12 months attending and participating in the group’s meetings and social events, and she interviewed several group members. In her study, Wilkins identified several ways in which the group “enforced” happiness—for example, by continually talking about happiness, discouraging the expression of negative emotions, and using happiness as a way to distinguish themselves from other groups.

One of the primary benefits of participant observation is that the researchers are in a much better position to understand the viewpoint and experiences of the people they are studying when they are a part of the social group. The primary limitation with this approach is that the mere presence of the observer could affect the behavior of the people being observed. While this is also a concern with naturalistic observation, additional concerns arise when researchers become active members of the social group they are studying because that they may change the social dynamics and/or influence the behavior of the people they are studying. Similarly, if the researcher acts as a participant observer there can be concerns with biases resulting from developing relationships with the participants. Concretely, the researcher may become less objective resulting in more experimenter bias.

Structured Observation

Another observational method is structured observation . Here the investigator makes careful observations of one or more specific behaviors in a particular setting that is more structured than the settings used in naturalistic or participant observation. Often the setting in which the observations are made is not the natural setting. Instead, the researcher may observe people in the laboratory environment. Alternatively, the researcher may observe people in a natural setting (like a classroom setting) that they have structured some way, for instance by introducing some specific task participants are to engage in or by introducing a specific social situation or manipulation.

Structured observation is very similar to naturalistic observation and participant observation in that in all three cases researchers are observing naturally occurring behavior; however, the emphasis in structured observation is on gathering quantitative rather than qualitative data. Researchers using this approach are interested in a limited set of behaviors. This allows them to quantify the behaviors they are observing. In other words, structured observation is less global than naturalistic or participant observation because the researcher engaged in structured observations is interested in a small number of specific behaviors. Therefore, rather than recording everything that happens, the researcher only focuses on very specific behaviors of interest.

Researchers Robert Levine and Ara Norenzayan used structured observation to study differences in the “pace of life” across countries (Levine & Norenzayan, 1999) [4] . One of their measures involved observing pedestrians in a large city to see how long it took them to walk 60 feet. They found that people in some countries walked reliably faster than people in other countries. For example, people in Canada and Sweden covered 60 feet in just under 13 seconds on average, while people in Brazil and Romania took close to 17 seconds. When structured observation  takes place in the complex and even chaotic “real world,” the questions of when, where, and under what conditions the observations will be made, and who exactly will be observed are important to consider. Levine and Norenzayan described their sampling process as follows:

“Male and female walking speed over a distance of 60 feet was measured in at least two locations in main downtown areas in each city. Measurements were taken during main business hours on clear summer days. All locations were flat, unobstructed, had broad sidewalks, and were sufficiently uncrowded to allow pedestrians to move at potentially maximum speeds. To control for the effects of socializing, only pedestrians walking alone were used. Children, individuals with obvious physical handicaps, and window-shoppers were not timed. Thirty-five men and 35 women were timed in most cities.” (p. 186).

Precise specification of the sampling process in this way makes data collection manageable for the observers, and it also provides some control over important extraneous variables. For example, by making their observations on clear summer days in all countries, Levine and Norenzayan controlled for effects of the weather on people’s walking speeds.  In Levine and Norenzayan’s study, measurement was relatively straightforward. They simply measured out a 60-foot distance along a city sidewalk and then used a stopwatch to time participants as they walked over that distance.

As another example, researchers Robert Kraut and Robert Johnston wanted to study bowlers’ reactions to their shots, both when they were facing the pins and then when they turned toward their companions (Kraut & Johnston, 1979) [5] . But what “reactions” should they observe? Based on previous research and their own pilot testing, Kraut and Johnston created a list of reactions that included “closed smile,” “open smile,” “laugh,” “neutral face,” “look down,” “look away,” and “face cover” (covering one’s face with one’s hands). The observers committed this list to memory and then practiced by coding the reactions of bowlers who had been videotaped. During the actual study, the observers spoke into an audio recorder, describing the reactions they observed. Among the most interesting results of this study was that bowlers rarely smiled while they still faced the pins. They were much more likely to smile after they turned toward their companions, suggesting that smiling is not purely an expression of happiness but also a form of social communication.

In yet another example (this one in a laboratory environment), Dov Cohen and his colleagues had observers rate the emotional reactions of participants who had just been deliberately bumped and insulted by a confederate after they dropped off a completed questionnaire at the end of a hallway. The confederate was posing as someone who worked in the same building and who was frustrated by having to close a file drawer twice in order to permit the participants to walk past them (first to drop off the questionnaire at the end of the hallway and once again on their way back to the room where they believed the study they signed up for was taking place). The two observers were positioned at different ends of the hallway so that they could read the participants’ body language and hear anything they might say. Interestingly, the researchers hypothesized that participants from the southern United States, which is one of several places in the world that has a “culture of honor,” would react with more aggression than participants from the northern United States, a prediction that was in fact supported by the observational data (Cohen, Nisbett, Bowdle, & Schwarz, 1996) [6] .

When the observations require a judgment on the part of the observers—as in the studies by Kraut and Johnston and Cohen and his colleagues—a process referred to as   coding is typically required . Coding generally requires clearly defining a set of target behaviors. The observers then categorize participants individually in terms of which behavior they have engaged in and the number of times they engaged in each behavior. The observers might even record the duration of each behavior. The target behaviors must be defined in such a way that guides different observers to code them in the same way. This difficulty with coding illustrates the issue of interrater reliability, as mentioned in Chapter 4. Researchers are expected to demonstrate the interrater reliability of their coding procedure by having multiple raters code the same behaviors independently and then showing that the different observers are in close agreement. Kraut and Johnston, for example, video recorded a subset of their participants’ reactions and had two observers independently code them. The two observers showed that they agreed on the reactions that were exhibited 97% of the time, indicating good interrater reliability.

One of the primary benefits of structured observation is that it is far more efficient than naturalistic and participant observation. Since the researchers are focused on specific behaviors this reduces time and expense. Also, often times the environment is structured to encourage the behaviors of interest which again means that researchers do not have to invest as much time in waiting for the behaviors of interest to naturally occur. Finally, researchers using this approach can clearly exert greater control over the environment. However, when researchers exert more control over the environment it may make the environment less natural which decreases external validity. It is less clear for instance whether structured observations made in a laboratory environment will generalize to a real world environment. Furthermore, since researchers engaged in structured observation are often not disguised there may be more concerns with reactivity.

Case Studies

A  case study   is an in-depth examination of an individual. Sometimes case studies are also completed on social units (e.g., a cult) and events (e.g., a natural disaster). Most commonly in psychology, however, case studies provide a detailed description and analysis of an individual. Often the individual has a rare or unusual condition or disorder or has damage to a specific region of the brain.

Like many observational research methods, case studies tend to be more qualitative in nature. Case study methods involve an in-depth, and often a longitudinal examination of an individual. Depending on the focus of the case study, individuals may or may not be observed in their natural setting. If the natural setting is not what is of interest, then the individual may be brought into a therapist’s office or a researcher’s lab for study. Also, the bulk of the case study report will focus on in-depth descriptions of the person rather than on statistical analyses. With that said some quantitative data may also be included in the write-up of a case study. For instance, an individual’s depression score may be compared to normative scores or their score before and after treatment may be compared. As with other qualitative methods, a variety of different methods and tools can be used to collect information on the case. For instance, interviews, naturalistic observation, structured observation, psychological testing (e.g., IQ test), and/or physiological measurements (e.g., brain scans) may be used to collect information on the individual.

HM is one of the most notorious case studies in psychology. HM suffered from intractable and very severe epilepsy. A surgeon localized HM’s epilepsy to his medial temporal lobe and in 1953 he removed large sections of his hippocampus in an attempt to stop the seizures. The treatment was a success, in that it resolved his epilepsy and his IQ and personality were unaffected. However, the doctors soon realized that HM exhibited a strange form of amnesia, called anterograde amnesia. HM was able to carry out a conversation and he could remember short strings of letters, digits, and words. Basically, his short term memory was preserved. However, HM could not commit new events to memory. He lost the ability to transfer information from his short-term memory to his long term memory, something memory researchers call consolidation. So while he could carry on a conversation with someone, he would completely forget the conversation after it ended. This was an extremely important case study for memory researchers because it suggested that there’s a dissociation between short-term memory and long-term memory, it suggested that these were two different abilities sub-served by different areas of the brain. It also suggested that the temporal lobes are particularly important for consolidating new information (i.e., for transferring information from short-term memory to long-term memory).

The history of psychology is filled with influential cases studies, such as Sigmund Freud’s description of “Anna O.” (see Note 6.1 “The Case of “Anna O.””) and John Watson and Rosalie Rayner’s description of Little Albert (Watson & Rayner, 1920) [7] , who allegedly learned to fear a white rat—along with other furry objects—when the researchers repeatedly made a loud noise every time the rat approached him.

The Case of “Anna O.”

Sigmund Freud used the case of a young woman he called “Anna O.” to illustrate many principles of his theory of psychoanalysis (Freud, 1961) [8] . (Her real name was Bertha Pappenheim, and she was an early feminist who went on to make important contributions to the field of social work.) Anna had come to Freud’s colleague Josef Breuer around 1880 with a variety of odd physical and psychological symptoms. One of them was that for several weeks she was unable to drink any fluids. According to Freud,

She would take up the glass of water that she longed for, but as soon as it touched her lips she would push it away like someone suffering from hydrophobia.…She lived only on fruit, such as melons, etc., so as to lessen her tormenting thirst. (p. 9)

But according to Freud, a breakthrough came one day while Anna was under hypnosis.

[S]he grumbled about her English “lady-companion,” whom she did not care for, and went on to describe, with every sign of disgust, how she had once gone into this lady’s room and how her little dog—horrid creature!—had drunk out of a glass there. The patient had said nothing, as she had wanted to be polite. After giving further energetic expression to the anger she had held back, she asked for something to drink, drank a large quantity of water without any difficulty, and awoke from her hypnosis with the glass at her lips; and thereupon the disturbance vanished, never to return. (p.9)

Freud’s interpretation was that Anna had repressed the memory of this incident along with the emotion that it triggered and that this was what had caused her inability to drink. Furthermore, he believed that her recollection of the incident, along with her expression of the emotion she had repressed, caused the symptom to go away.

As an illustration of Freud’s theory, the case study of Anna O. is quite effective. As evidence for the theory, however, it is essentially worthless. The description provides no way of knowing whether Anna had really repressed the memory of the dog drinking from the glass, whether this repression had caused her inability to drink, or whether recalling this “trauma” relieved the symptom. It is also unclear from this case study how typical or atypical Anna’s experience was.

Case studies are useful because they provide a level of detailed analysis not found in many other research methods and greater insights may be gained from this more detailed analysis. As a result of the case study, the researcher may gain a sharpened understanding of what might become important to look at more extensively in future more controlled research. Case studies are also often the only way to study rare conditions because it may be impossible to find a large enough sample of individuals with the condition to use quantitative methods. Although at first glance a case study of a rare individual might seem to tell us little about ourselves, they often do provide insights into normal behavior. The case of HM provided important insights into the role of the hippocampus in memory consolidation.

However, it is important to note that while case studies can provide insights into certain areas and variables to study, and can be useful in helping develop theories, they should never be used as evidence for theories. In other words, case studies can be used as inspiration to formulate theories and hypotheses, but those hypotheses and theories then need to be formally tested using more rigorous quantitative methods. The reason case studies shouldn’t be used to provide support for theories is that they suffer from problems with both internal and external validity. Case studies lack the proper controls that true experiments contain. As such, they suffer from problems with internal validity, so they cannot be used to determine causation. For instance, during HM’s surgery, the surgeon may have accidentally lesioned another area of HM’s brain (a possibility suggested by the dissection of HM’s brain following his death) and that lesion may have contributed to his inability to consolidate new information. The fact is, with case studies we cannot rule out these sorts of alternative explanations. So, as with all observational methods, case studies do not permit determination of causation. In addition, because case studies are often of a single individual, and typically an abnormal individual, researchers cannot generalize their conclusions to other individuals. Recall that with most research designs there is a trade-off between internal and external validity. With case studies, however, there are problems with both internal validity and external validity. So there are limits both to the ability to determine causation and to generalize the results. A final limitation of case studies is that ample opportunity exists for the theoretical biases of the researcher to color or bias the case description. Indeed, there have been accusations that the woman who studied HM destroyed a lot of her data that were not published and she has been called into question for destroying contradictory data that didn’t support her theory about how memories are consolidated. There is a fascinating New York Times article that describes some of the controversies that ensued after HM’s death and analysis of his brain that can be found at: https://www.nytimes.com/2016/08/07/magazine/the-brain-that-couldnt-remember.html?_r=0

Archival Research

Another approach that is often considered observational research involves analyzing archival data that have already been collected for some other purpose. An example is a study by Brett Pelham and his colleagues on “implicit egotism”—the tendency for people to prefer people, places, and things that are similar to themselves (Pelham, Carvallo, & Jones, 2005) [9] . In one study, they examined Social Security records to show that women with the names Virginia, Georgia, Louise, and Florence were especially likely to have moved to the states of Virginia, Georgia, Louisiana, and Florida, respectively.

As with naturalistic observation, measurement can be more or less straightforward when working with archival data. For example, counting the number of people named Virginia who live in various states based on Social Security records is relatively straightforward. But consider a study by Christopher Peterson and his colleagues on the relationship between optimism and health using data that had been collected many years before for a study on adult development (Peterson, Seligman, & Vaillant, 1988) [10] . In the 1940s, healthy male college students had completed an open-ended questionnaire about difficult wartime experiences. In the late 1980s, Peterson and his colleagues reviewed the men’s questionnaire responses to obtain a measure of explanatory style—their habitual ways of explaining bad events that happen to them. More pessimistic people tend to blame themselves and expect long-term negative consequences that affect many aspects of their lives, while more optimistic people tend to blame outside forces and expect limited negative consequences. To obtain a measure of explanatory style for each participant, the researchers used a procedure in which all negative events mentioned in the questionnaire responses, and any causal explanations for them were identified and written on index cards. These were given to a separate group of raters who rated each explanation in terms of three separate dimensions of optimism-pessimism. These ratings were then averaged to produce an explanatory style score for each participant. The researchers then assessed the statistical relationship between the men’s explanatory style as undergraduate students and archival measures of their health at approximately 60 years of age. The primary result was that the more optimistic the men were as undergraduate students, the healthier they were as older men. Pearson’s  r  was +.25.

This method is an example of  content analysis —a family of systematic approaches to measurement using complex archival data. Just as structured observation requires specifying the behaviors of interest and then noting them as they occur, content analysis requires specifying keywords, phrases, or ideas and then finding all occurrences of them in the data. These occurrences can then be counted, timed (e.g., the amount of time devoted to entertainment topics on the nightly news show), or analyzed in a variety of other ways.

Media Attributions

• What happens when you remove the hippocampus? – Sam Kean by TED-Ed licensed under a standard YouTube License
• Pappenheim 1882  by unknown is in the  Public Domain .
• Festinger, L., Riecken, H., & Schachter, S. (1956). When prophecy fails: A social and psychological study of a modern group that predicted the destruction of the world. University of Minnesota Press. ↵
• Rosenhan, D. L. (1973). On being sane in insane places. Science, 179 , 250–258. ↵
• Wilkins, A. (2008). “Happier than Non-Christians”: Collective emotions and symbolic boundaries among evangelical Christians. Social Psychology Quarterly, 71 , 281–301. ↵
• Levine, R. V., & Norenzayan, A. (1999). The pace of life in 31 countries. Journal of Cross-Cultural Psychology, 30 , 178–205. ↵
• Kraut, R. E., & Johnston, R. E. (1979). Social and emotional messages of smiling: An ethological approach. Journal of Personality and Social Psychology, 37 , 1539–1553. ↵
• Cohen, D., Nisbett, R. E., Bowdle, B. F., & Schwarz, N. (1996). Insult, aggression, and the southern culture of honor: An "experimental ethnography." Journal of Personality and Social Psychology, 70 (5), 945-960. ↵
• Watson, J. B., & Rayner, R. (1920). Conditioned emotional reactions. Journal of Experimental Psychology, 3 , 1–14. ↵
• Freud, S. (1961).  Five lectures on psycho-analysis . New York, NY: Norton. ↵
• Pelham, B. W., Carvallo, M., & Jones, J. T. (2005). Implicit egotism. Current Directions in Psychological Science, 14 , 106–110. ↵
• Peterson, C., Seligman, M. E. P., & Vaillant, G. E. (1988). Pessimistic explanatory style is a risk factor for physical illness: A thirty-five year longitudinal study. Journal of Personality and Social Psychology, 55 , 23–27. ↵

Research that is non-experimental because it focuses on recording systemic observations of behavior in a natural or laboratory setting without manipulating anything.

An observational method that involves observing people’s behavior in the environment in which it typically occurs.

When researchers engage in naturalistic observation by making their observations as unobtrusively as possible so that participants are not aware that they are being studied.

Where the participants are made aware of the researcher presence and monitoring of their behavior.

Refers to when a measure changes participants’ behavior.

In the case of undisguised naturalistic observation, it is a type of reactivity when people know they are being observed and studied, they may act differently than they normally would.

Researchers become active participants in the group or situation they are studying.

Researchers pretend to be members of the social group they are observing and conceal their true identity as researchers.

Researchers become a part of the group they are studying and they disclose their true identity as researchers to the group under investigation.

When a researcher makes careful observations of one or more specific behaviors in a particular setting that is more structured than the settings used in naturalistic or participant observation.

A part of structured observation whereby the observers use a clearly defined set of guidelines to "code" behaviors—assigning specific behaviors they are observing to a category—and count the number of times or the duration that the behavior occurs.

An in-depth examination of an individual.

A family of systematic approaches to measurement using qualitative methods to analyze complex archival data.

Research Methods in Psychology Copyright © 2019 by Rajiv S. Jhangiani, I-Chant A. Chiang, Carrie Cuttler, & Dana C. Leighton is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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• Chamberlain University Library
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Finding Types of Research

• Observational Studies

On This Guide

• Evidence-Based Practice
• Quantitative and Qualitative
• Systematic Reviews and Meta-Analysis
• Randomized Controlled Trials

What are Observational Studies?

Find observational studies, use database observational studies filters, review observational studies examples.

• Literature Reviews
• Finding Research Tools This link opens in a new window

Observational studies are conducted in a natural environment where the purpose is to observe outcomes without enacting change. They can be either qualitative or quantitative. You will find several different types of observational studies in your research.

• Case-Control:  Subjects with a disease or condition are compared with those who don’t. There is an effort to match the case and control on different things that may influence the comparison. 
• Cohort:  A group of participants is chosen and followed over time to look at specific outcomes. Cohort studies can either be prospective, where the group is chosen and followed over time, or retrospective, where the researcher selects a group of participants and looks at a time period in the past.
• Cross-Sectional:  It looks at the variables of interest in a group of participants at one point in time. Participants are not followed over time.

One way to find observational studies is to add a keyword of the study type to your search strategy either when using the Search Everything box on the library homepage or the individual databases on the Database A to Z page.

• diabetes AND case-control
• hypertension AND cross-sectional
• poverty AND (teen OR youth OR adolescent) AND cohort

There are also filters in specific databases to limit your search results to case reports, case studies, or observational studies only. The next section of this page will explain how to use those filters in these databases.

PubMed: Observational Studies Filters 

• Database - PubMed PubMed is an online source provided by the National Library of Medicine that features millions of citations for biomedical literature from Medline, life science journals, and eBooks. Citations may include links to full-text content from the Chamberlain Library, PubMed Central, and publisher websites.

To find observational studies in PubMed, follow the steps below.

• Go to the database using the link above.
• Enter your keyword(s) into the search box.
• Select Search .
• Select  Additional Filters  at the bottom of the filters list on the left-hand side of the page, as shown in the image below. 

• In the pop-up window, select  Article Type and choose either Case Reports or Observational Study , as shown in the image below.

Medline Complete: Observational Studies Filters

• Database - Medline Complete from EBSCO Medline Complete provides access to journals covering a wide range of topics in the biomedical sciences and medicine.

To find observational studies in Medline, follow the steps below.

• Select the database link above to open the Advanced Search page.
• Enter your keyword(s) into the search boxes.  
• Move down to the Limit Your Results  section. This is where you can add filters to your search to tell the database that you only want articles that fit specific criteria. 
• From the Publication Type  menu, select either Case Reports , Case Study , or Observational Study , as shown in the image below.

• After you have your results, make sure to read through the article abstracts to see which type of study design the researchers are using. There is no guarantee that the database will only generate these types of studies.

Case-Control

• Article - Association Between ABCB1 Gene Polymorphism and Renal Function in Patients with Hypertension: A Case-Control Study. Chen, X., Zhou, T., Yang, D., & Lu, J. (2017). Association between ABCB1 gene polymorphism and renal function in patients with hypertension: A case-control Study. Medical Science Monitor, 23, 3854–3860. https://doi.org/10.12659/MSM.902954
• Article - Association of Long-Term Exposure to Transportation Noise and Traffic-Related Air Pollution with the Incidence of Diabetes: A Prospective Cohort Study. Clark, C., Sbihi, H., Tamburic, L., Brauer, M., Frank, L.D., & Davies, H.W. (2017). Association of long-term exposure to transportation noise and traffic-related air pollution with the incidence of diabetes: A prospective cohort study. Environmental Health Perspectives, 125(8), 087025. https://doi.org/10.1289/EHP1279
• Article - A Safety Comparison of Metformin vs Sulfonylurea Initiation in Patients With Type 2 Diabetes and Chronic Kidney Disease: A Retrospective Cohort Study Whitlock, R.H., Hougen, I., Komenda, P., Rigatto, C., Clemens, K.K., & Tangri, N. (2020). A safety comparison of Metformin vs Sulfonylurea initiation in patients with type 2 diabetes and chronic kidney disease: A retrospective cohort study. Mayo Clinic Proceedings, 95(1), 90–100. https://doi.org/10.1016/j.mayocp.2019.07.017

Cross-Sectional

• Article - Preferences for models of peer support in the digital era: A cross-sectional survey of people with cancer. Boyes, A., Turon, H., Hall, A., Watson, R., Proietto, A., & Sanson‐Fisher, R. (2018). Preferences for models of peer support in the digital era: A cross‐sectional survey of people with cancer. Psycho-Oncology, 27 (9), 2148–2154. https://doi.org/10.1002/pon.4781
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An introduction to different types of study design

Posted on 6th April 2021 by Hadi Abbas

Study designs are the set of methods and procedures used to collect and analyze data in a study.

Broadly speaking, there are 2 types of study designs: descriptive studies and analytical studies.

Descriptive studies

• Describes specific characteristics in a population of interest
• The most common forms are case reports and case series
• In a case report, we discuss our experience with the patient’s symptoms, signs, diagnosis, and treatment
• In a case series, several patients with similar experiences are grouped.

Analytical Studies

Analytical studies are of 2 types: observational and experimental.

Observational studies are studies that we conduct without any intervention or experiment. In those studies, we purely observe the outcomes.  On the other hand, in experimental studies, we conduct experiments and interventions.

Observational studies

Observational studies include many subtypes. Below, I will discuss the most common designs.

Cross-sectional study:

• This design is transverse where we take a specific sample at a specific time without any follow-up
• It allows us to calculate the frequency of disease ( p revalence ) or the frequency of a risk factor
• This design is easy to conduct
• For example – if we want to know the prevalence of migraine in a population, we can conduct a cross-sectional study whereby we take a sample from the population and calculate the number of patients with migraine headaches.

Cohort study:

• We conduct this study by comparing two samples from the population: one sample with a risk factor while the other lacks this risk factor
• It shows us the risk of developing the disease in individuals with the risk factor compared to those without the risk factor ( RR = relative risk )
• Prospective : we follow the individuals in the future to know who will develop the disease
• Retrospective : we look to the past to know who developed the disease (e.g. using medical records)
• This design is the strongest among the observational studies
• For example – to find out the relative risk of developing chronic obstructive pulmonary disease (COPD) among smokers, we take a sample including smokers and non-smokers. Then, we calculate the number of individuals with COPD among both.

Case-Control Study:

• We conduct this study by comparing 2 groups: one group with the disease (cases) and another group without the disease (controls)
• This design is always retrospective
•  We aim to find out the odds of having a risk factor or an exposure if an individual has a specific disease (Odds ratio)
•  Relatively easy to conduct
• For example – we want to study the odds of being a smoker among hypertensive patients compared to normotensive ones. To do so, we choose a group of patients diagnosed with hypertension and another group that serves as the control (normal blood pressure). Then we study their smoking history to find out if there is a correlation.

Experimental Studies

• Also known as interventional studies
• Can involve animals and humans
• Pre-clinical trials involve animals
• Clinical trials are experimental studies involving humans
• In clinical trials, we study the effect of an intervention compared to another intervention or placebo. As an example, I have listed the four phases of a drug trial:

I:  We aim to assess the safety of the drug ( is it safe ? )

II: We aim to assess the efficacy of the drug ( does it work ? )

III: We want to know if this drug is better than the old treatment ( is it better ? )

IV: We follow-up to detect long-term side effects ( can it stay in the market ? )

• In randomized controlled trials, one group of participants receives the control, while the other receives the tested drug/intervention. Those studies are the best way to evaluate the efficacy of a treatment.

Finally, the figure below will help you with your understanding of different types of study designs.

References (pdf)

You may also be interested in the following blogs for further reading:

An introduction to randomized controlled trials

Case-control and cohort studies: a brief overview

Cohort studies: prospective and retrospective designs

Prevalence vs Incidence: what is the difference?

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No Comments on An introduction to different types of study design

you are amazing one!! if I get you I’m working with you! I’m student from Ethiopian higher education. health sciences student

Very informative and easy understandable

You are my kind of doctor. Do not lose sight of your objective.

Wow very erll explained and easy to understand

I’m Khamisu Habibu community health officer student from Abubakar Tafawa Balewa university teaching hospital Bauchi, Nigeria, I really appreciate your write up and you have make it clear for the learner. thank you

well understood,thank you so much

Well understood…thanks

Simply explained. Thank You.

Thanks a lot for this nice informative article which help me to understand different study designs that I felt difficult before

That’s lovely to hear, Mona, thank you for letting the author know how useful this was. If there are any other particular topics you think would be useful to you, and are not already on the website, please do let us know.

it is very informative and useful.

thank you statistician

Fabulous to hear, thank you John.

Thanks for this information

Thanks so much for this information….I have clearly known the types of study design Thanks

That’s so good to hear, Mirembe, thank you for letting the author know.

Very helpful article!! U have simplified everything for easy understanding

I’m a health science major currently taking statistics for health care workers…this is a challenging class…thanks for the simified feedback.

That’s good to hear this has helped you. Hopefully you will find some of the other blogs useful too. If you see any topics that are missing from the website, please do let us know!

Hello. I liked your presentation, the fact that you ranked them clearly is very helpful to understand for people like me who is a novelist researcher. However, I was expecting to read much more about the Experimental studies. So please direct me if you already have or will one day. Thank you

Dear Ay. My sincere apologies for not responding to your comment sooner. You may find it useful to filter the blogs by the topic of ‘Study design and research methods’ – here is a link to that filter: https://s4be.cochrane.org/blog/topic/study-design/ This will cover more detail about experimental studies. Or have a look on our library page for further resources there – you’ll find that on the ‘Resources’ drop down from the home page.

However, if there are specific things you feel you would like to learn about experimental studies, that are missing from the website, it would be great if you could let me know too. Thank you, and best of luck. Emma

Great job Mr Hadi. I advise you to prepare and study for the Australian Medical Board Exams as soon as you finish your undergrad study in Lebanon. Good luck and hope we can meet sometime in the future. Regards ;)

You have give a good explaination of what am looking for. However, references am not sure of where to get them from.

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Observation Method in Psychology: Naturalistic, Participant and Controlled

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The observation method in psychology involves directly and systematically witnessing and recording measurable behaviors, actions, and responses in natural or contrived settings without attempting to intervene or manipulate what is being observed.

Used to describe phenomena, generate hypotheses, or validate self-reports, psychological observation can be either controlled or naturalistic with varying degrees of structure imposed by the researcher.

There are different types of observational methods, and distinctions need to be made between:

1. Controlled Observations 2. Naturalistic Observations 3. Participant Observations

In addition to the above categories, observations can also be either overt/disclosed (the participants know they are being studied) or covert/undisclosed (the researcher keeps their real identity a secret from the research subjects, acting as a genuine member of the group).

In general, conducting observational research is relatively inexpensive, but it remains highly time-consuming and resource-intensive in data processing and analysis.

The considerable investments needed in terms of coder time commitments for training, maintaining reliability, preventing drift, and coding complex dynamic interactions place practical barriers on observers with limited resources.

Controlled Observation

Controlled observation is a research method for studying behavior in a carefully controlled and structured environment.

The researcher sets specific conditions, variables, and procedures to systematically observe and measure behavior, allowing for greater control and comparison of different conditions or groups.

The researcher decides where the observation will occur, at what time, with which participants, and in what circumstances, and uses a standardized procedure. Participants are randomly allocated to each independent variable group.

Rather than writing a detailed description of all behavior observed, it is often easier to code behavior according to a previously agreed scale using a behavior schedule (i.e., conducting a structured observation).

The researcher systematically classifies the behavior they observe into distinct categories. Coding might involve numbers or letters to describe a characteristic or the use of a scale to measure behavior intensity.

The categories on the schedule are coded so that the data collected can be easily counted and turned into statistics.

For example, Mary Ainsworth used a behavior schedule to study how infants responded to brief periods of separation from their mothers. During the Strange Situation procedure, the infant’s interaction behaviors directed toward the mother were measured, e.g.,

• Proximity and contact-seeking
• Contact maintaining
• Avoidance of proximity and contact
• Resistance to contact and comforting

The observer noted down the behavior displayed during 15-second intervals and scored the behavior for intensity on a scale of 1 to 7.

Sometimes participants’ behavior is observed through a two-way mirror, or they are secretly filmed. Albert Bandura used this method to study aggression in children (the Bobo doll studies ).

A lot of research has been carried out in sleep laboratories as well. Here, electrodes are attached to the scalp of participants. What is observed are the changes in electrical activity in the brain during sleep ( the machine is called an EEG ).

Controlled observations are usually overt as the researcher explains the research aim to the group so the participants know they are being observed.

Controlled observations are also usually non-participant as the researcher avoids direct contact with the group and keeps a distance (e.g., observing behind a two-way mirror).

• Controlled observations can be easily replicated by other researchers by using the same observation schedule. This means it is easy to test for reliability .
• The data obtained from structured observations is easier and quicker to analyze as it is quantitative (i.e., numerical) – making this a less time-consuming method compared to naturalistic observations.
• Controlled observations are fairly quick to conduct which means that many observations can take place within a short amount of time. This means a large sample can be obtained, resulting in the findings being representative and having the ability to be generalized to a large population.

Limitations

• Controlled observations can lack validity due to the Hawthorne effect /demand characteristics. When participants know they are being watched, they may act differently.

Naturalistic Observation

Naturalistic observation is a research method in which the researcher studies behavior in its natural setting without intervention or manipulation.

It involves observing and recording behavior as it naturally occurs, providing insights into real-life behaviors and interactions in their natural context.

Naturalistic observation is a research method commonly used by psychologists and other social scientists.

This technique involves observing and studying the spontaneous behavior of participants in natural surroundings. The researcher simply records what they see in whatever way they can.

In unstructured observations, the researcher records all relevant behavior with a coding system. There may be too much to record, and the behaviors recorded may not necessarily be the most important, so the approach is usually used as a pilot study to see what type of behaviors would be recorded.

Compared with controlled observations, it is like the difference between studying wild animals in a zoo and studying them in their natural habitat.

With regard to human subjects, Margaret Mead used this method to research the way of life of different tribes living on islands in the South Pacific. Kathy Sylva used it to study children at play by observing their behavior in a playgroup in Oxfordshire.

Collecting Naturalistic Behavioral Data

Technological advances are enabling new, unobtrusive ways of collecting naturalistic behavioral data.

The Electronically Activated Recorder (EAR) is a digital recording device participants can wear to periodically sample ambient sounds, allowing representative sampling of daily experiences (Mehl et al., 2012).

Studies program EARs to record 30-50 second sound snippets multiple times per hour. Although coding the recordings requires extensive resources, EARs can capture spontaneous behaviors like arguments or laughter.

EARs minimize participant reactivity since sampling occurs outside of awareness. This reduces the Hawthorne effect, where people change behavior when observed.

The SenseCam is another wearable device that passively captures images documenting daily activities. Though primarily used in memory research currently (Smith et al., 2014), systematic sampling of environments and behaviors via the SenseCam could enable innovative psychological studies in the future.

• By being able to observe the flow of behavior in its own setting, studies have greater ecological validity.
• Like case studies , naturalistic observation is often used to generate new ideas. Because it gives the researcher the opportunity to study the total situation, it often suggests avenues of inquiry not thought of before.
• The ability to capture actual behaviors as they unfold in real-time, analyze sequential patterns of interactions, measure base rates of behaviors, and examine socially undesirable or complex behaviors that people may not self-report accurately.
• These observations are often conducted on a micro (small) scale and may lack a representative sample (biased in relation to age, gender, social class, or ethnicity). This may result in the findings lacking the ability to generalize to wider society.
• Natural observations are less reliable as other variables cannot be controlled. This makes it difficult for another researcher to repeat the study in exactly the same way.
• Highly time-consuming and resource-intensive during the data coding phase (e.g., training coders, maintaining inter-rater reliability, preventing judgment drift).
• With observations, we do not have manipulations of variables (or control over extraneous variables), meaning cause-and-effect relationships cannot be established.

Participant Observation

Participant observation is a variant of the above (natural observations) but here, the researcher joins in and becomes part of the group they are studying to get a deeper insight into their lives.

If it were research on animals , we would now not only be studying them in their natural habitat but be living alongside them as well!

Leon Festinger used this approach in a famous study into a religious cult that believed that the end of the world was about to occur. He joined the cult and studied how they reacted when the prophecy did not come true.

Participant observations can be either covert or overt. Covert is where the study is carried out “undercover.” The researcher’s real identity and purpose are kept concealed from the group being studied.

The researcher takes a false identity and role, usually posing as a genuine member of the group.

On the other hand, overt is where the researcher reveals his or her true identity and purpose to the group and asks permission to observe.

• It can be difficult to get time/privacy for recording. For example, researchers can’t take notes openly with covert observations as this would blow their cover. This means they must wait until they are alone and rely on their memory. This is a problem as they may forget details and are unlikely to remember direct quotations.
• If the researcher becomes too involved, they may lose objectivity and become biased. There is always the danger that we will “see” what we expect (or want) to see. This problem is because they could selectively report information instead of noting everything they observe. Thus reducing the validity of their data.

Recording of Data

With controlled/structured observation studies, an important decision the researcher has to make is how to classify and record the data. Usually, this will involve a method of sampling.

In most coding systems, codes or ratings are made either per behavioral event or per specified time interval (Bakeman & Quera, 2011).

The three main sampling methods are:

Event-based coding involves identifying and segmenting interactions into meaningful events rather than timed units.

For example, parent-child interactions may be segmented into control or teaching events to code. Interval recording involves dividing interactions into fixed time intervals (e.g., 6-15 seconds) and coding behaviors within each interval (Bakeman & Quera, 2011).

Event recording allows counting event frequency and sequencing while also potentially capturing event duration through timed-event recording. This provides information on time spent on behaviors.

• Interval recording is common in microanalytic coding to sample discrete behaviors in brief time samples across an interaction. The time unit can range from seconds to minutes to whole interactions. Interval recording requires segmenting interactions based on timing rather than events (Bakeman & Quera, 2011).
• Instantaneous sampling provides snapshot coding at certain moments rather than summarizing behavior within full intervals. This allows quicker coding but may miss behaviors in between target times.

Coding Systems

The coding system should focus on behaviors, patterns, individual characteristics, or relationship qualities that are relevant to the theory guiding the study (Wampler & Harper, 2014).

Codes vary in how much inference is required, from concrete observable behaviors like frequency of eye contact to more abstract concepts like degree of rapport between a therapist and client (Hill & Lambert, 2004). More inference may reduce reliability.

Coding schemes can vary in their level of detail or granularity. Micro-level schemes capture fine-grained behaviors, such as specific facial movements, while macro-level schemes might code broader behavioral states or interactions. The appropriate level of granularity depends on the research questions and the practical constraints of the study.

Another important consideration is the concreteness of the codes. Some schemes use physically based codes that are directly observable (e.g., “eyes closed”), while others use more socially based codes that require some level of inference (e.g., “showing empathy”). While physically based codes may be easier to apply consistently, socially based codes often capture more meaningful behavioral constructs.

Most coding schemes strive to create sets of codes that are mutually exclusive and exhaustive (ME&E). This means that for any given set of codes, only one code can apply at a time (mutual exclusivity), and there is always an applicable code (exhaustiveness). This property simplifies both the coding process and subsequent data analysis.

For example, a simple ME&E set for coding infant state might include: 1) Quiet alert, 2) Crying, 3) Fussy, 4) REM sleep, and 5) Deep sleep. At any given moment, an infant would be in one and only one of these states.

Macroanalytic coding systems

Macroanalytic coding systems involve rating or summarizing behaviors using larger coding units and broader categories that reflect patterns across longer periods of interaction rather than coding small or discrete behavioral acts. 

Macroanalytic coding systems focus on capturing overarching themes, global qualities, or general patterns of behavior rather than specific, discrete actions.

For example, a macroanalytic coding system may rate the overall degree of therapist warmth or level of client engagement globally for an entire therapy session, requiring the coders to summarize and infer these constructs across the interaction rather than coding smaller behavioral units.

These systems require observers to make more inferences (more time-consuming) but can better capture contextual factors, stability over time, and the interdependent nature of behaviors (Carlson & Grotevant, 1987).

Examples of Macroanalytic Coding Systems:

• Emotional Availability Scales (EAS) : This system assesses the quality of emotional connection between caregivers and children across dimensions like sensitivity, structuring, non-intrusiveness, and non-hostility.
• Classroom Assessment Scoring System (CLASS) : Evaluates the quality of teacher-student interactions in classrooms across domains like emotional support, classroom organization, and instructional support.

Microanalytic coding systems

Microanalytic coding systems involve rating behaviors using smaller, more discrete coding units and categories.

These systems focus on capturing specific, discrete behaviors or events as they occur moment-to-moment. Behaviors are often coded second-by-second or in very short time intervals.

For example, a microanalytic system may code each instance of eye contact or head nodding during a therapy session. These systems code specific, molecular behaviors as they occur moment-to-moment rather than summarizing actions over longer periods.

Microanalytic systems require less inference from coders and allow for analysis of behavioral contingencies and sequential interactions between therapist and client. However, they are more time-consuming and expensive to implement than macroanalytic approaches.

Examples of Microanalytic Coding Systems:

• Facial Action Coding System (FACS) : Codes minute facial muscle movements to analyze emotional expressions.
• Specific Affect Coding System (SPAFF) : Used in marital interaction research to code specific emotional behaviors.
• Noldus Observer XT : A software system that allows for detailed coding of behaviors in real-time or from video recordings.

Mesoanalytic coding systems

Mesoanalytic coding systems attempt to balance macro- and micro-analytic approaches.

In contrast to macroanalytic systems that summarize behaviors in larger chunks, mesoanalytic systems use medium-sized coding units that target more specific behaviors or interaction sequences (Bakeman & Quera, 2017).

For example, a mesoanalytic system may code each instance of a particular type of therapist statement or client emotional expression. However, mesoanalytic systems still use larger units than microanalytic approaches coding every speech onset/offset.

The goal of balancing specificity and feasibility makes mesoanalytic systems well-suited for many research questions (Morris et al., 2014). Mesoanalytic codes can preserve some sequential information while remaining efficient enough for studies with adequate but limited resources.

For instance, a mesoanalytic couple interaction coding system could target key behavior patterns like validation sequences without coding turn-by-turn speech.

In this way, mesoanalytic coding allows reasonable reliability and specificity without requiring extensive training or observation. The mid-level focus offers a pragmatic compromise between depth and breadth in analyzing interactions.

Examples of Mesoanalytic Coding Systems:

• Feeding Scale for Mother-Infant Interaction : Assesses feeding interactions in 5-minute episodes, coding specific behaviors and overall qualities.
• Couples Interaction Rating System (CIRS): Codes specific behaviors and rates overall qualities in segments of couple interactions.
• Teaching Styles Rating Scale : Combines frequency counts of specific teacher behaviors with global ratings of teaching style in classroom segments.

Preventing Coder Drift

Coder drift results in a measurement error caused by gradual shifts in how observations get rated according to operational definitions, especially when behavioral codes are not clearly specified.

This type of error creeps in when coders fail to regularly review what precise observations constitute or do not constitute the behaviors being measured.

Preventing drift refers to taking active steps to maintain consistency and minimize changes or deviations in how coders rate or evaluate behaviors over time. Specifically, some key ways to prevent coder drift include:

• Operationalize codes : It is essential that code definitions unambiguously distinguish what interactions represent instances of each coded behavior. 
• Ongoing training : Returning to those operational definitions through ongoing training serves to recalibrate coder interpretations and reinforce accurate recognition. Having regular “check-in” sessions where coders practice coding the same interactions allows monitoring that they continue applying codes reliably without gradual shifts in interpretation.
• Using reference videos : Coders periodically coding the same “gold standard” reference videos anchors their judgments and calibrate against original training. Without periodic anchoring to original specifications, coder decisions tend to drift from initial measurement reliability.
• Assessing inter-rater reliability : Statistical tracking that coders maintain high levels of agreement over the course of a study, not just at the start, flags any declines indicating drift. Sustaining inter-rater agreement requires mitigating this common tendency for observer judgment change during intensive, long-term coding tasks.
• Recalibrating through discussion : Having meetings for coders to discuss disagreements openly explores reasons judgment shifts may be occurring over time. Consensus on the application of codes is restored.
• Adjusting unclear codes : If reliability issues persist, revisiting and refining ambiguous code definitions or anchors can eliminate inconsistencies arising from coder confusion.

Essentially, the goal of preventing coder drift is maintaining standardization and minimizing unintentional biases that may slowly alter how observational data gets rated over periods of extensive coding.

Through the upkeep of skills, continuing calibration to benchmarks, and monitoring consistency, researchers can notice and correct for any creeping changes in coder decision-making over time.

Reducing Observer Bias

Observational research is prone to observer biases resulting from coders’ subjective perspectives shaping the interpretation of complex interactions (Burghardt et al., 2012). When coding, personal expectations may unconsciously influence judgments. However, rigorous methods exist to reduce such bias.

Coding Manual

A detailed coding manual minimizes subjectivity by clearly defining what behaviors and interaction dynamics observers should code (Bakeman & Quera, 2011).

High-quality manuals have strong theoretical and empirical grounding, laying out explicit coding procedures and providing rich behavioral examples to anchor code definitions (Lindahl, 2001).

Clear delineation of the frequency, intensity, duration, and type of behaviors constituting each code facilitates reliable judgments and reduces ambiguity for coders. Application risks inconsistency across raters without clarity on how codes translate to observable interaction.

Coder Training

Competent coders require both interpersonal perceptiveness and scientific rigor (Wampler & Harper, 2014). Training thoroughly reviews the theoretical basis for coded constructs and teaches the coding system itself.

Multiple “gold standard” criterion videos demonstrate code ranges that trainees independently apply. Coders then meet weekly to establish reliability of 80% or higher agreement both among themselves and with master criterion coding (Hill & Lambert, 2004).

Ongoing training manages coder drift over time. Revisions to unclear codes may also improve reliability. Both careful selection and investment in rigorous training increase quality control.

Blind Methods

To prevent bias, coders should remain unaware of specific study predictions or participant details (Burghardt et al., 2012). Separate data gathering versus coding teams helps maintain blinding.

Coders should be unaware of study details or participant identities that could bias coding (Burghardt et al., 2012).

Separate teams collecting data versus coding data can reduce bias.

In addition, scheduling procedures can prevent coders from rating data collected directly from participants with whom they have had personal contact. Maintaining coder independence and blinding enhances objectivity.

Data Analysis Approaches

Data analysis in behavioral observation aims to transform raw observational data into quantifiable measures that can be statistically analyzed.

It’s important to note that the choice of analysis approach is not arbitrary but should be guided by the research questions, study design, and nature of the data collected.

Interval data (where behavior is recorded at fixed time points), event data (where the occurrence of behaviors is noted as they happen), and timed-event data (where both the occurrence and duration of behaviors are recorded) may require different analytical approaches.

Similarly, the level of measurement (categorical, ordinal, or continuous) will influence the choice of statistical tests.

Researchers typically start with simple descriptive statistics to get a feel for their data before moving on to more complex analyses. This stepwise approach allows for a thorough understanding of the data and can often reveal unexpected patterns or relationships that merit further investigation.

simple descriptive statistics

Descriptive statistics give an overall picture of behavior patterns and are often the first step in analysis.

• Frequency counts tell us how often a particular behavior occurs, while rates express this frequency in relation to time (e.g., occurrences per minute).
• Duration measures how long behaviors last, offering insight into their persistence or intensity.
• Probability calculations indicate the likelihood of a behavior occurring under certain conditions, and relative frequency or duration statistics show the proportional occurrence of different behaviors within a session or across the study.

These simple statistics form the foundation of behavioral analysis, providing researchers with a broad picture of behavioral patterns. 

They can reveal which behaviors are most common, how long they typically last, and how they might vary across different conditions or subjects.

For instance, in a study of classroom behavior, these statistics might show how often students raise their hands, how long they typically stay focused on a task, or what proportion of time is spent on different activities.

contingency analyses

Contingency analyses help identify if certain behaviors tend to occur together or in sequence.

• Contingency tables , also known as cross-tabulations, display the co-occurrence of two or more behaviors, allowing researchers to see if certain behaviors tend to happen together.
• Odds ratios provide a measure of the strength of association between behaviors, indicating how much more likely one behavior is to occur in the presence of another.
• Adjusted residuals in these tables can reveal whether the observed co-occurrences are significantly different from what would be expected by chance.

For example, in a study of parent-child interactions, contingency analyses might reveal whether a parent’s praise is more likely to follow a child’s successful completion of a task, or whether a child’s tantrum is more likely to occur after a parent’s refusal of a request.

These analyses can uncover important patterns in social interactions, learning processes, or behavioral chains.

sequential analyses

Sequential analyses are crucial for understanding processes and temporal relationships between behaviors.

• Lag sequential analysis looks at the likelihood of one behavior following another within a specified number of events or time units.
• Time-window sequential analysis examines whether a target behavior occurs within a defined time frame after a given behavior.

These methods are particularly valuable for understanding processes that unfold over time, such as conversation patterns, problem-solving strategies, or the development of social skills.

observer agreement

Since human observers often code behaviors, it’s important to check reliability . This is typically done through measures of observer agreement.

• Cohen’s kappa is commonly used for categorical data, providing a measure of agreement between observers that accounts for chance agreement.
• Intraclass correlation coefficient (ICC) : Used for continuous data or ratings.

Good observer agreement is crucial for the validity of the study, as it demonstrates that the observed behaviors are consistently identified and coded across different observers or time points.

advanced statistical approaches

As researchers delve deeper into their data, they often employ more advanced statistical techniques.

• For instance, an ANOVA might reveal differences in the frequency of aggressive behaviors between children from different socioeconomic backgrounds or in different school settings.
• This approach allows researchers to account for dependencies in the data and to examine how behaviors might be influenced by factors at different levels (e.g., individual characteristics, group dynamics, and situational factors).
• This method can reveal trends, cycles, or patterns in behavior over time, which might not be apparent from simpler analyses. For instance, in a study of animal behavior, time series analysis might uncover daily or seasonal patterns in feeding, mating, or territorial behaviors.

representation techniques

Representation techniques help organize and visualize data:

• Many researchers use a code-unit grid, which represents the data as a matrix with behaviors as rows and time units as columns.
• This format facilitates many types of analyses and allows for easy visualization of behavioral patterns.
• Standardized formats like the Sequential Data Interchange Standard (SDIS) help ensure consistency in data representation across studies and facilitate the use of specialized analysis software.
• Indeed, the complexity of behavioral observation data often necessitates the use of specialized software tools. Programs like GSEQ, Observer, and INTERACT are designed specifically for the analysis of observational data and can perform many of the analyses described above efficiently and accurately.

Bakeman, R., & Quera, V. (2017). Sequential analysis and observational methods for the behavioral sciences. Cambridge University Press.

Burghardt, G. M., Bartmess-LeVasseur, J. N., Browning, S. A., Morrison, K. E., Stec, C. L., Zachau, C. E., & Freeberg, T. M. (2012). Minimizing observer bias in behavioral studies: A review and recommendations. Ethology, 118 (6), 511-517.

Hill, C. E., & Lambert, M. J. (2004). Methodological issues in studying psychotherapy processes and outcomes. In M. J. Lambert (Ed.), Bergin and Garfield’s handbook of psychotherapy and behavior change (5th ed., pp. 84–135). Wiley.

Lindahl, K. M. (2001). Methodological issues in family observational research. In P. K. Kerig & K. M. Lindahl (Eds.), Family observational coding systems: Resources for systemic research (pp. 23–32). Lawrence Erlbaum Associates.

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Case Study Observational Research: A Framework for Conducting Case Study Research Where Observation Data Are the Focus

Affiliation.

• 1 1 University of Otago, Wellington, New Zealand.
• PMID: 27217290
• DOI: 10.1177/1049732316649160

Case study research is a comprehensive method that incorporates multiple sources of data to provide detailed accounts of complex research phenomena in real-life contexts. However, current models of case study research do not particularly distinguish the unique contribution observation data can make. Observation methods have the potential to reach beyond other methods that rely largely or solely on self-report. This article describes the distinctive characteristics of case study observational research, a modified form of Yin's 2014 model of case study research the authors used in a study exploring interprofessional collaboration in primary care. In this approach, observation data are positioned as the central component of the research design. Case study observational research offers a promising approach for researchers in a wide range of health care settings seeking more complete understandings of complex topics, where contextual influences are of primary concern. Future research is needed to refine and evaluate the approach.

Keywords: New Zealand; appreciative inquiry; case studies; case study observational research; health care; interprofessional collaboration; naturalistic inquiry; observation; primary health care; qualitative; research design.

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• Published: 22 August 2024

Incidence of anxiety after traumatic brain injury: a systematic review and meta-analysis

• Masoud Dehbozorgi 1 ,
• Mohammad Reza Maghsoudi 2 , 3 ,
• Ida Mohammadi 4 ,
• Shahryar Rajai Firouzabadi 4 ,
• Mahdi Mohammaditabar 3 ,
• Soroush Oraee 4 ,
• Aryan Aarabi 4 ,
• Mana Goodarzi 4 ,
• Arman Shafiee 2 , 3 , 5 &
• Mahmood Bakhtiyari 2  

BMC Neurology volume  24 , Article number:  293 ( 2024 ) Cite this article

Metrics details

Traumatic brain injury (TBI) is defined as acquired cerebral damage caused by an external mechanical impact, which has the potential to lead to transient or enduring debilitation. TBI is associated with many forms of long-lasting psychiatric conditions, including anxiety disorders. As anxiety is highly debilitating by causing impaired social functioning and decreased quality of life for the afflicted, especially in the form of anxiety disorders such as generalized anxiety disorder, certain efforts have been made to explore the factors associated with it, and one such factor is TBI.

We searched PubMed, Scopus, and Web of Science on January 26th, 2024 for observational case–control or cohort or cross-sectional studies assessing the incidence of anxiety symptoms or disorders in patients with TBI compared to healthy individuals or the same individuals if pre-TBI information regarding anxiety was available. We calculated the pooled incidence and relative risk (RR) and 95% confidence interval (95CI) using the inverse variance method. Publication bias was assessed using Eggers’s regression test. Quality assessment was performed using the Newcastle–Ottawa scale. Sub-group analyses were conducted for the type of anxiety (anxiety disorder vs anxiety symptoms), TBI severity, and type of anxiety disorders.

The incidence rate of anxiety after traumatic brain injury was 17.45% (95CI: 12.59%, 22.31%) in a total of 705,024 individuals. Moreover, TBI patients were found to be 1.9 times as likely to have anxiety compared to their non-TBI counterparts [Random effects model RR = 1.90 [1.62; 2.23], p -value < 0.0001] using a population of 569,875 TBI cases and 1,640,312 non-TBI controls. Sub-group analysis revealed TBI severity was not associated with anxiety and generalized anxiety disorder was the most common type of anxiety disorder reported post-TBI.

Patients who have experienced a TBI exhibit a significantly greater incidence of anxiety symptoms and anxiety disorders in the aftermath when compared to healthy individuals.

Peer Review reports

Introduction

Traumatic brain injury (TBI) is an acquired insult to the brain from an external mechanical force that may result in temporary or permanent impairment [ 1 ]. TBI has the highest incidence of all common neurological disorders [ 2 ], thus constituting a major public health concern. In 2019 alone, TBI had 27.16 million new cases, 48.99 million prevalent cases, and 7.08 million Years lived with disability (YLDs) globally [ 3 ]. It is notable that TBI was viewed as an injury event with finite recovery in the past. However, it is now recognized as a chronic condition that can impact various domains of health and function, with some even deteriorating over time [ 4 ]. One such important aspect impacted by TBI is neuropsychiatric health. TBI has been recognized as a condition with long-lasting neuropsychiatric consequences including major depressive disorder, post-traumatic stress disorder, and anxiety disorders [ 5 , 6 ]. Be that as it may, the incidence, extent, and management of these negative sequelae have not been fully defined and require further examination [ 7 ].

Anxiety is a prevalent neuropsychiatric illness, with approximately 4.05% of the worldwide populace affected by an anxiety disorder [ 8 ]. In addition, anxiety disorders can have debilitating effects on an individual’s life including reduced work productivity, diminished social functioning, and decreased quality of life [ 9 ]. As a result, extensive efforts have been dedicated to uncovering the root causes of anxiety and mitigating its impact by addressing underlying predisposing factors [ 10 , 11 ]. One such predisposing factor is TBI, as pooled prevalence estimates indicate a high long-term prevalence of anxiety disorders (36%) among TBI patients [ 12 ].

Although the prevalence and risk factors of anxiety among TBI patients have been assessed thorough a systematic review [ 12 ], the incidence and risk of developing anxiety after TBI _relative to non-TBI counterparts_ have not yet been systematically quantified across the literature. This quantification is crucial as awareness of this likelihood can aid physicians in providing patients with the necessary medical care. As a result, we set out to determine the incidence of developing anxiety symptomology and disorders in patients with a history of TBI and to compare the risk with a non-TBI patient population.

This systematic review and meta-analysis was conducted according to PRISMA guidelines [ 13 ]. The protocol for this review was prospectively submitted on PROSPERO (ID: CRD42024519155).

Search strategy

A comprehensive search of 3 online databases (Pubmed, Scopus, and Web of Science) was conducted on January 26th, 2024, using Mesh terms and keywords synonymous with “Traumatic brain injury” and “Anxiety.” No language or publication year limitations were defined.

Selection criteria

Papers were included if they complied with our predefined PECOS of (P) individuals with a self-reported or author-confirmed history of traumatic brain injury; (E) traumatic brain injury as defined by the authors; (C) healthy controls without TBI during the study period or the same individuals if pre-TBI information regarding Anxiety was available; (O): Anxiety symptomology, self-reported feelings of Anxiety, or diagnosis of Anxiety using a validated assessment scale; (S): observational case–control or cohort or cross-sectional studies.

Exclusion criteria were: (P) participants without confirmed brain trauma; (E) traumatic brain pathology not consistent with traumatic brain injury; (C) no healthy controls and no information regarding pre-TBI Anxiety symptoms or diagnoses; (O) Anxiety symptomology or disorder reported in tandem with other mental illnesses or any illness proven to cause anxiety; (S): in vitro studies, in vivo studies, controlled trials, case reports, and case series. Studies that used the same population were separated, and the one with the larger population was included.

Study selection

Papers were screened for selection independently by two reviewers in 2 phases—an initial title and abstract screening followed by full-text retrieval and screening. The inconsistencies were addressed by consulting with a third reviewer and discussing them.

Data extraction

Two reviewers performed data extraction independently using a pre-constructed spreadsheet, with independent validation by a third reviewer. Discrepancies were resolved through discussion. The following items were extracted: author, year of publication, country of first author, sample origin, study type, definition of TBI, type of TBI (mild or moderate or severe TBI), mean/median of age, sample size, male (%), anxiety assessment scale, time of study, and time of follow up from TBI. Missing data were marked as not provided (NP). The type of anxiety was also extracted and categorized into anxiety disorders (i.e. generalized anxiety disorder, panic disorder) or anxiety symptoms above clinical cut-off values. The type of anxiety disorder was also extracted if provided separately. Due to the inclusion of papers utilizing previous versions of the Diagnostic and Statistical Manual of Mental Disorders, which categorized obsessive compulsive disorder (OCD) and post-traumatic stress disorder (PTSD) as anxiety disorders, we decided to included such diagnoses under the definition of anxiety disorders as well. For the purposes of this review, concussions were categorized as mild traumatic brain injuries.

Data regarding our outcome of interest, the incidence of anxiety, was extracted in two domains: novel cases of Anxiety and total participants in the TBI cohort or anxiety cases and full participants in the TBI and healthy control groups. If data regarding multiple time points were available, the last time point of the Anxiety assessment was extracted.

Risk of bias assessment

The risk of bias in the included observational studies was assessed using the Newcastle–Ottawa scale (NOS) [ 14 ]. 2 reviewers independently evaluated the quality of the papers using NOS in three domains: selection, comparability, and exposure. The inconsistencies were resolved by conversing with a third reviewer. A total score of < 7 was deemed a high risk of bias.

Statistical analysis

Meta-analysis and statistical analysis were performed using R studio's ‘meta’ package by drawing a forest plot. Between-study heterogeneity was investigated using the I 2 test since an I 2 of around 25%, 50%, and 75% is considered low, moderate, and high levels of heterogeneity, respectively. Using the inverse variance method, the pooled relative risk (RR) and 95% confidence interval (95CI) were calculated. Both The common (fixed) effect model and random-effects model were calculated, and in case of heterogeneity the random effects model was reported Significance was defined as p -value < 0.05. Also, pooled proportion meta-analyses were performed on eligible studies using the inverse variance method. Finally, publication bias was assessed using Egger’s regression test. In case multiple time-points were reported, the longest follow-up was used in our analyses.

Sub-group analyses based on the type of TBI (mild vs moderate-severe), the type of anxiety (anxiety disorders vs anxiety symptoms), and the type of anxiety disorder were conducted to investigated heterogeneity.

Our online database search yielded a total of 2318 papers, of which 1732 were chosen for title-abstract screening after the removal of 586 duplicates. Of these 121 were chosen for full-text retrieval and evaluation and 49 were included in this review. Of the excluded papers, 3 were excluded due to their TBI population suffering from comorbidities (dementia), 1 was excluded due to using a mixed sample of TBI and blast-exposed veterans, one was excluded due to using a mixed sample of TBI and spinal cord injuries, and 68 were excluded due to reporting of means and standard deviations for anxiety assessment scales instead of the number of participants above the scales cut-off or reporting the prevalence of anxiety after TBI and not its incidence (Fig.  1 ).

PRISMA flowchart

22 of the included studies were cohort investigations, 26 were case–control studies, and one study was cross-sectional in design. The studies investigated TBIs that occurred between the 1980s – 2022 and assessed anxiety 1 week [ 15 ] to 24 years [ 16 ] after TBI. TBI was most commonly defined using the American Congress of Rehabilitation Medicines’ definition in 15 studies (loss of consciousness < 30 min or Glasgow coma scale 13–15 or post-traumatic amnesia < 24 h or focal neurological deficits) while 10 studies utilized ICD codes for TBI instead. The type of TBI reported was mostly mTBI, followed by a mixture of varying TBI severities (any TBI).

Most of the included studies used an American population ( n  = 26) while 8 studies used an Australian population instead. The investigated TBI populations mostly had a mean/median age of 30–40 years ( n  = 18) while 11 studies included children or adolescents. 11 of the studies included mostly males in their TBI sample (male > 75%) and one study used a mostly female sample (male < 25%).

Anxiety symptom assessment scales used in the studies varied significantly, with 7 using Hospital Anxiety and Depression Scale (HADS), 3 using diagnostic interview schedule (DIS), 2 using Beck’s Anxiety Inventory (BAI), 2 using Neurobehavioral Symptom Inventory (NSI), 2 using the Mini-International Neuropsychiatric Interview (MINI), 2 using the Structured Clinical Interview for DSM (SCID), yet most of the studies ( n  = 9) used ICD codes for anxiety instead. The type of anxiety investigated was mostly anxiety symptoms ( n  = 26), while 23 of the included studies investigated anxiety disorders. The types of anxiety disorders investigated were mostly generalized anxiety disorder (GAD) ( n  = 15), followed by post-traumatic stress disorder (PTSD) and panic disorder (Table  1 ).

Incidence of Anxiety

Among the included studies, 20 papers reported novel cases of anxiety after TBI, the pooled proportion showed an incidence rate of 17.45% (95CI: 12.59%, 22.31%), albeit with significant heterogeneity in a total of 705,024 individuals (I 2  = 100.0%, Fig.  2 ). Publication bias was not evident according to Egger’s test ( p -value = 0.08). When investigating heterogeneity, 16 studies had reported the incidence of anxiety disorders and 4 studies reported anxiety symptoms, which did not vary significantly. 15 reports of the type of TBI showed no significant difference between mTBI and moderate-severe TBI, and 8 studies had reported the type of anxiety disorders, which showed significant difference between disorders, with GAD and PTSD being the most common disorders and OCD and social anxiety being the least prevalent (Table  2 ).

Results of meta-analysis for incident anxiety post traumatic brain injury

Relative risk of anxiety

33 of the included studies compared anxiety in 569,875 TBI cases with 1,640,312 non-TBI controls, the pooled results of which revealed TBI patients were 1.9 times as likely to be anxious compared to their non-TBI counterparts (RR = 1.90 (95CI: 1.62; 2.23), p -value < 0.0001), with high heterogeneity (I 2  = 99%) (Fig. 3 ). Egger’s regression test found no evidence of publication bias in the meta-analysis ( p -value = 0.184). When investigating heterogeneity, TBI patients were more likely to report anxiety disorders compared to anxiety symptoms when comparing 11 studies assessing anxiety disorders to 21 studies assessing anxiety symptoms. No difference regarding TBI severity and anxiety was evident when comparing 21 mTBI studies to 7 moderate-severe TBI studies. Only 3 studies had reported the type of anxiety disorders and sub-group analysis was therefore avoided (Table  3 ).

Results of meta-analysis for risk of anxiety symptoms after traumatic brain injury compared to control group

Quality assessment

Of the 22 cohort studies included, 12 were judged to be of high quality and 10 were of low quality. Furthermore, of the 26 case–control studies, 13 were determined to be high-quality while 13 were of low quality. The single cross-sectional study included was also judged to be of low quality (Figure S1-3).

To the best of our knowledge, this is the first systematic review to calculate the incidence of anxiety symptoms following TBI. Pooling the results of 20 studies investigating novel cases of anxiety following TBI culminated in an incidence rate of 17.45% among 705,024 TBI patients. 2 reports to date have systematically examined anxiety in relation to TBI. The study by Osborn et al. [ 64 ] encompassed 41 studies and demonstrated that 11% of participants were diagnosed with generalized anxiety disorder and 37% reported clinically significant levels of anxiety following TBI. Moreover, anxiety diagnoses and symptomology were most prevalent 2 to 5 years post-injury. On the other hand, Scholten et al. [ 12 ] by excluding self-reported anxiety, estimated that the pooled prevalence of anxiety was 19% prior to TBI, and 21% in the first year after TBI. Pooled prevalence estimates increased over time and indicated high long-term prevalence of anxiety disorders (36%). Our meta-analysis is notable in that it provides the first estimate of the incidence of novel anxiety symptoms in the literature, helping to better understand this association. It should be noted that this pooled estimate was marked by a high degree of heterogeneity, which could be partly attributed to diagnostic criteria, interview schedules, and self-report measures, as noted by Osborn et al. The severity of TBI is another potential factor that may have contributed to this heterogeneity, yet our sub-group analyses showed this was not the case. Further direct comparisons are needed in the matter to ascertain our findings, especially on a moderate-severe TBI population. The type of anxiety appears to be a potential factor explaining the heterogeneity, with TBI patients being at a higher risk for anxiety disorders compared to anxiety symptoms. Additionally, we found that the types of anxiety disorders developed after TBI appear to be mostly GAD and PTSD, and rarely social anxiety and OCD. This is in accordance with the previous literature, estimating that 3%—28% develop of TBI patients GAD, 13%—24% develop PTSD, and 2%—15% develop OCD [ 65 ].

Another main finding of this report was that pooling the data from 33 reports among 569,875 TBI patients and 1,640,312 non-TBI controls indicated a significant, 1.9-fold risk of anxiety symptomology relative to non-TBI counterparts, albeit with a high degree of heterogeneity. A similar analysis was previously conducted by Osborn et al. [ 64 ], calculating an odds ratio of 2.46 of developing clinically significant anxiety symptoms by pooling the data from 10 studies comparing TBI patients and control groups.

A notable consideration in studies evaluating TBI as a potential risk factor for anxiety disorders was the relatively substantial prevalence of pre-existing psychiatric conditions among patients who sustained TBIs, implying what appears to be circular causality between TBIs and psychiatric disorders [ 66 ]. In addition, While evidence concerning whether TBIs lead to substance use disorder (SUD) remains inconclusive, there is a presumption that individuals with SUD are generally at a higher risk of experiencing TBIs [ 67 , 68 ]. As a result, a part of the increased rate of anxiety symptoms may stem from the pre-existing effects of substance use.

Various explanations have been proposed regarding the heightened risk of anxiety disorders following TBI. The first hypothesis is that anxiety symptoms following TBI may be attributed to a reduction in hippocampal size. Wilde et al. utilized imaging techniques to evaluate morphometric changes in children post-TBI, revealing remarkable volume loss in the hippocampus, amygdala, and globus pallidus, with the hippocampus being particularly affected to a larger extent, implying widespread hippocampal damage in TBI [ 69 ]. Consistent with this evidence, MRI reports have indicated reduced hippocampal size in patients with certain anxiety disorders [ 70 , 71 ]. The second explanation is the alterations that occur in the structure and function of the amygdala, which is hallmarked by a significant decline in GABAergic interneurons leading to a lower level of inhibition and consequently increased excitability in the amygdala. Additionally, heightened expression and activation of nicotinic acetylcholine receptors were observed post-TBI, exacerbating neuronal excitability within the basolateral amygdala. These mechanisms, among others, collectively contribute to hyperexcitability in the amygdala, ultimately inducing anxiety symptoms [ 72 , 73 , 74 , 75 , 76 ]. Other explanations, such as the dysregulation of the hypothalamo-pituitary-adrenal axis [ 77 , 78 , 79 ], remain incompletely recognized at the current state of the art and further research in this regard is necessary.

Our review has several limitations. Firstly, variations in the methodology of the included studies have resulted in a high degree of heterogeneity in our analysis, warranting exercising caution when interpreting our results. In addition, the observational nature of most studies included in this review restricts our ability to draw conclusions about cause-and-effect relationships. Lastly, while we aimed to explore the relative risk of each type of anxiety disorder and their correlation with TBI severity, there was not sufficient data to explore these aspects of the disorders in more depth.

To conclude, our systematic review and meta-analysis revealed a remarkable relative risk of anxiety symptoms in patients sustained TBI, supporting existing research that identifies TBI as a risk factor for anxiety disorders. In addition, we reported the incidence of anxiety symptoms in patients who sustained TBI. These results have implications for planning interventions for individuals with TBI. However, the limitations in explaining this association and the significant heterogeneity in our study underscore the need for cautious interpretation of these findings. Given the limitations and uncertainties in our conclusions, further research is recommended to establish more robust evidence in this context.

Availability of data and materials

All data has been presented in the manuscript.

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Acknowledgements

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Data Statement

The data has not been previously presented orally or by poster at scientific meetings.

This study did not receive funding, grant, or sponsorship from any individuals or organizations.

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The Faculty of Medicine, RWTH Aachen University, Aachen, Germany

Masoud Dehbozorgi

Non-Communicable Diseases Research Center, Alborz University of Medical Sciences, Karaj, Iran

Mohammad Reza Maghsoudi, Arman Shafiee & Mahmood Bakhtiyari

Student Research Committee, School of Medicine, Alborz University of Medical Sciences, Karaj, Iran

Mohammad Reza Maghsoudi, Mahdi Mohammaditabar & Arman Shafiee

Shahid Beheshti University of Medical Sciences, Tehran, Iran

Ida Mohammadi, Shahryar Rajai Firouzabadi, Soroush Oraee, Aryan Aarabi & Mana Goodarzi

Alborz University of Medical Sciences, Karaj, Iran

Arman Shafiee

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A.S, M.D, MR.M.: Conceptualization, Project Administration, Data curation, Writing- Original Draft, Writing – Review & Editing, Visualization I.M, M.D, S.R, M.M: Validation, Resources, Methodology, Software, Formal analysis, Writing – Original Draft S.O, A.A, M.G: Writing- Original Draft M.B.: Data curation.

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Dehbozorgi, M., Maghsoudi, M.R., Mohammadi, I. et al. Incidence of anxiety after traumatic brain injury: a systematic review and meta-analysis. BMC Neurol 24 , 293 (2024). https://doi.org/10.1186/s12883-024-03791-0

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The effect of metformin on peripheral nerve morphology in type 2 diabetes: a cross-sectional observational study

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Roshan Dhanapalaratnama , Tushar Issar , Leiao Leon Wang , Darren Tran , Ann M. Poynten , Kerry-Lee Milner , Natalie CG. Kwai , Arun V. Krishnan; The effect of metformin on peripheral nerve morphology in type 2 diabetes: a cross-sectional observational study. Diabetes 2024; db240365. https://doi.org/10.2337/db24-0365

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Diabetic peripheral neuropathy (DPN) affects around 50% of the 500 million people with type 2 diabetes worldwide and is considered disabling and irreversible. The present study was undertaken to assess the effect of metformin on peripheral neuropathy outcomes in type 2 diabetes. 69 type 2 diabetes participants receiving metformin were recruited and underwent clinical assessment, peripheral nerve ultrasound, nerve conduction studies and axonal excitability studies. 318 participants who were not on metformin were also concurrently screened, and 69 were selected as disease controls and matched to the metformin participants for age, sex, diabetes duration, BMI, HbA1c and use of other diabetes therapies. Medical record data over the previous 20 years were analysed for previous metformin use. Mean tibial nerve cross-sectional area (CSA) was lower in the metformin group (metformin 14.1 ∓ 0.7 mm2, non-metformin 16.2 ∓ 0.9mm2, p=0.038), accompanied by reduction in neuropathy symptom severity (p=0.021). Axonal excitability studies demonstrated superior axonal function in the metformin group and mathematical modelling demonstrated that these improvements were mediated by changes in nodal Na + and K + conductances. Metformin treatment is associated with superior nerve structure, clinical and neurophysiological measures. Treatment with metformin may be neuroprotective in DPN.

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Spatial transcriptomics profiling of gallbladder adenocarcinoma: a detailed two-case study of progression from precursor lesions to cancer

• Sophie Pirenne 1   na1   nAff2 ,
• Fátima Manzano-Núñez 1   na1 ,
• Axelle Loriot 1 ,
• Sabine Cordi 1 ,
• Lieven Desmet 3 ,
• Selda Aydin 4 , 5 ,
• Catherine Hubert 4 , 6 ,
• Sébastien Toffoli 7 ,
• Nisha Limaye 1 ,
• Christine Sempoux 8 ,
• Mina Komuta 9 ,
• Laurent Gatto 1 &
• Frédéric P. Lemaigre 1  

BMC Cancer volume  24 , Article number:  1025 ( 2024 ) Cite this article

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Most studies on tumour progression from precursor lesion toward gallbladder adenocarcinoma investigate lesions sampled from distinct patients, providing an overarching view of pathogenic cascades. Whether this reflects the tumourigenic process in individual patients remains insufficiently explored. Genomic and epigenomic studies suggest that a subset of gallbladder cancers originate from biliary intraepithelial neoplasia (BilIN) precursor lesions, whereas others form independently from BilINs. Spatial transcriptomic data supporting these conclusions are missing. Moreover, multiple areas with precursor or adenocarcinoma lesions can be detected within the same pathological sample. Yet, knowledge about intra-patient variability of such lesions is lacking.

To characterise the spatial transcriptomics of gallbladder cancer tumourigenesis in individual patients, we selected two patients with distinct cancer aetiology and whose samples simultaneously displayed multiple areas of normal epithelium, BilINs and adenocarcinoma. Using GeoMx digital spatial profiling, we characterised the whole transcriptome of a high number of regions of interest (ROIs) per sample in the two patients (24 and 32 ROIs respectively), with each ROI covering approximately 200 cells of normal epithelium, low-grade BilIN, high-grade BilIN or adenocarcinoma. Human gallbladder organoids and cell line-derived tumours were used to investigate the tumour-promoting role of genes.

Spatial transcriptomics revealed that each type of lesion displayed limited intra-patient transcriptomic variability. Our data further suggest that adenocarcinoma derived from high-grade BilIN in one patient and from low-grade BilIN in the other patient, with co-existing high-grade BilIN evolving via a distinct process in the latter case. The two patients displayed distinct sequences of signalling pathway activation during tumour progression, but Semaphorin 4 A ( SEMA4A ) expression was repressed in both patients. Using human gallbladder-derived organoids and cell line-derived tumours, we provide evidence that repression of SEMA4A promotes pseudostratification of the epithelium and enhances cell migration and survival.

Gallbladder adenocarcinoma can develop according to patient-specific processes, and limited intra-patient variability of precursor and cancer lesions was noticed. Our data suggest that repression of SEMA4A can promote tumour progression. They also highlight the need to gain gene expression data in addition to histological information to avoid understimating the risk of low-grade preneoplastic lesions.

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Gallbladder cancer accounts for less than 2% of cancer-related deaths and is often fortuitously diagnosed in gallbladder samples following cholecystectomy. The prognosis of the disease remains poor because patients often present at an advanced stage with unresectable tumour. Late diagnosis results from the lack of specific symptoms and of screening strategies, as well as from limited knowledge of the mechanisms driving tumour progression [ 1 , 2 ]. Several studies investigated the pathology, genomics and epigenomics of tumour progression from precursor to cancer stage. They mostly investigated precursor and cancer lesions from distinct patients, precluding a good understanding of tumour progression at the individual level. Spatial transcriptomic data on precursor and adenocarcinoma lesions coexisting in a same patient are expected to provide clues on the mechanisms of tumour progression.

Adenocarcinomas account for > 90% of gallbladder cancers and are considered to develop according to a metaplasia-dysplasia-adenocarcinoma histogenic sequence, in which the dysplastic stage consists of low-grade and high-grade biliary intraepithelial neoplasia (BilIN) [ 3 , 4 , 5 , 6 , 7 , 8 ]. BilINs consist of microscopic, flat or micropapillary lesions whose grade depends on the highest degree of cytological and architectural atypia. Low-grade BilINs display moderate cytoarchitectural atypia with pseudostratification of the nuclei, increased nucleo-cytoplasmic ratio and hyperchromasia. High-grade BilINs, formerly called carcinomas in situ, are defined by loss of nuclear polarity, marked cytological atypia and complex architectural patterns such as micropapillae [ 9 , 10 , 11 ].

Genomic alterations are already found at the BilIN stage. KRAS and TP53 mutations were found in BilINs [ 12 , 13 ] and a progressive increase in TP53 overexpression was proposed to occur during the evolution from low-grade BilIN to GBC [ 14 ]. A recent exome sequencing study uncovered CTNNB1 , TP53 , ARID2 and ERBB3 as the most frequently mutated genes in low-grade and high-grade BilINs [ 15 ]. When the disease evolves to invasive adenocarcinoma, alterations accumulate, and tumours display significant cell type heterogeneity [ 16 , 17 ]. At that stage the most frequent mutations affect KRAS , CTNNB1 , TP53 , PI3KCA , ERBB2 , CDKN2A and CDKN2B [ 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 ], indicating that a fraction of the mutations found at the cancer stage can be detected in BilIN lesions. At the epigenome level, cancer lesions were split in subtypes with distinct hypermethylation: hypomethylation ratios; progressive and cumulative changes in promoter methylation were detected during progression from cholecystitis to cancer [ 26 , 27 , 28 , 29 ]. Increased hypermethylation was observed in adenocarcinomas as compared to BilINs. These epigenomic changes impacted Wnt/β-catenin signalling, Hedgehog signalling, tumour suppression and cell-microenvironment interactions [ 30 , 31 , 32 ]. Further, since gallstone-induced chronic inflammation drives gallbladder carcinogenesis [ 33 ], several authors compared the transcriptome of normal gallbladder tissue, gallbladder cancer, and gallbladder tissue exposed for varying lengths of time to gallstones, and identified molecular signatures associated with disease progression [ 34 , 35 ]. Finally, in line with the genomic and epigenomic studies, single gene analyses revealed aberrant expression levels of TP53, P21, cyclin D1, EZH2, SMAD4 and CDKN2A protein at the BilIN stage [ 11 ], as well as the ability of a combined activation of KRAS and canonical Wnt/β-catenin or Notch signalling to induce gallbladder BilINs with malignant potential [ 36 , 37 ]. Spatial transcriptomic data investigating BilIN to adenocarcinoma progression are still lacking.

Considering the genomics of tumour progression, Lin and coworkers provided evidence for patient-specific tumourigenic processes [ 15 ]. Their results indicated that precursor and cancer lesions within the same patient bear similar mutations, whereas the mutational signatures significantly vary between patients. Phylogenetic analysis of single nucleotide variants in lesions generated revealed that gallbladder cancer developed either BilIN-dependently or BilIN-independently [ 15 ].

To address the spatial transcriptomics of gallbladder tumour progression in individual patients, we selected samples from two patients displaying simultaneously several areas of gallbladder BilINs and adenocarcinoma, and collected an extensive spatial transcriptomic data set of each type of lesion per patient. The two patients were selected because of their differing cancer aetiology, offering the possibility to address intra-patient variability and tumour progression in distinct contexts. Our results show that each type of lesion displayed limited variability within the same patient, but significantly differed among patients. This revealed that the two patients have distinct tumourigenic processes, thereby corroborating earlier conclusions at the transcriptomic level. Our molecular investigations using gallbladder organoids also provide evidence that Semaphorin 4 A ( SEMA4A ) repression, which was observed in the two patients, can contribute to tumour progression.

• Spatial transcriptomics

Spatial profiling was performed on formalin-fixed paraffin-embedded (FFPE) tissue sections using GeoMx (NanoString Technologies, Seattle, WA, USA) [ 38 ] which was implemented by NanoString. The GeoMx Whole Transcriptome Atlas assay probe cocktail containing 18,677 probes was tested. Regions of interest (ROIs) subjected to spatial transcriptomic profiling encompassed epithelial areas of approximately 200 cells. The 24 ROIs of Patient #1 were all located on the same tissue section. For Patient #2, the ROIs were partitioned over two sections, namely 8 ROIs covering normal epithelium on one section, and 24 ROIs covering lesional tissue on a second section (Table  1 ). Additional information is provided in Additional file 1 (Supplementary methods).

Histology and staining

Hematoxylin/Eosin (H&E) and Sirius red/fast green stainings were performed on 6 μm sections of FFPE tissues, tumours or organoids. Briefly, tissue sections were deparaffinised 3 × 3 min in xylene, 3 min in 99%, 95%, 70% and 30% ethanol and deionised H 2 O. Sections were stained 7 s in 100% hematoxylin, rinsed with H 2 O, stained for 7 s in 100% eosine, and rinsed with deionised H 2 O. Dehydration of sections was performed in deionised H 2 O, followed by 30%, 70%, 95%, 99% ethanol for 30 s, and 30 s in xylene. For collagen staining, slides were incubated into a picric acid solution with Sirius Red (Direct Red 80, Sigma-Aldrich) and Fast Green (SigmaAldrich) following manufacturer’s instructions. Coverslips were placed on slides using Depex mounting medium (VWR, Leuven, Belgium). Pictures were taken with panoramic P250 Digital Slide Scanner (Histogenex, Antwerpen, Belgium) using 3DHISTECH Case Viewer software.

Immunofluorescence and immunohistochemistry

Immunofluorescence and immunohistochemistry were performed on 6 μm sections of FFPE tissues. FFPE tissue sections were deparaffinised 3 × 3 min in xylene, 2 min in 99%, 95%, 70% and 30% ethanol and deionised water. Antigen retrieval was performed by the use of Lab Vision PT Module (Thermo Fisher Scientific, Waltham, MA), in 10 mM citrate pH 6. Sections were permeabilised for 10 min in 0.3% Triton X-100 in PBS before blocking for 1 h in 5% HS,10% BSA, 0.3% Triton X-100 in PBS. Primary antibodies were diluted in blocking solution at 4 °C overnight and secondary antibodies were diluted in 10% BSA, 0.3% Triton X-100 in PBS at 37 °C for 1 h. Images were taken with panoramic P250 Digital Slide Scanner (Histogenex, Antwerpen, Belgium) using 3DHISTECH Case Viewer software. Primary and secondary antibodies are described in Additional file 1 (Supplementary Table S1 ).

RNAscope in situ hybridisation

RNAScope RNA in situ hybridisation was performed on 5 μm sections of FFPE tissues, according to the manufacturer’s protocol for manual RNAscope®2.5 HD Assay—RED (#322360, Advanced Cell Diagnostics/Bio-Techne, Abingdon, United Kingdom). The tissue sections were incubated at 60 °C for 1h30, deparaffinised 2 × 5 min in xylene and dehydrated 2 × 2 min in 99% ethanol. Endogenous peroxidase was blocked with hydrogen peroxide for 10 min at room temperature followed by two short washings with deionised water. Slides were heated for 10 s at 100 °C in deionised water, and antigen retrieval was performed for 15 min at 100 °C using RNAscope®Target retrieval. Tissue sections were washed in deionised water and 99% ethanol. Slides were dried for 5 min at room temperature and tissues were delineated using an ImmEdge Hydrophobic Barrier Pen (#310018, Advanced Cell Diagnostics/Bio-Techne, Abingdon, United Kingdom). Slides were incubated for 15 min with RNAscope®Protease plus (diluted at 1/5 in deionised water) at 40 °C, washed with deionised water and incubated with the Hs-COL1A1-Homo sapiens collagen type I alpha 1 mRNA probe for 2 h at 40 °C. The tissue sections were washed with RNAscope®Wash buffer and six amplifications were performed (using six reagents AMP1-AMP6). The signal detection followed using RNAscope®Fast A and B reagents for 10 min at RT. The slides were kept in phosphate-buffered saline (PBS) overnight and immunostaining was performed: sections were blocked for 45 min at room temperature in 3% milk, 10% bovine serum albumin (BSA), 0.3% Triton in PBS. Primary and secondary antibodies were diluted in 10% BSA, 0.3% Triton in PBS. Primary antibodies were incubated overnight at 4 °C and secondary antibodies were incubated 1h30 at 37 °C. Pictures were taken with Cell Observer Spinning Disk (Carl Zeiss, Zaventem, Belgium) and analysed with Zen blue software. Primary and secondary antibodies are described in Additional file 1 (Supplementary Table S1 ).

Gallbladder organoid culture

Human non-tumoral gallbladder tissues were obtained from patients who underwent cholecystectomy at the Cliniques Universitaires Saint-Luc, Brussels, using the method of Rimland and coworkers [ 39 ]. The karyotype of the selected organoid line was normal and whole exome sequencing detected an ERBB3 R675G missense mutation at an allelic fraction of 0.021. The COSMIC database of somatic mutations in cancer does not report this variant which we considered as non-contributory to our experiments. To analyse the impact of blocking SEMA4A in gallbladder organoids, the latter were split and plated. After 24 h, SEMA4A antibody (IgG-SEMA4A, #14-1002-82 eBioscience/Thermo Fisher scientific, Brussels, Belgium) was added into the medium (10 µg/ml) and organoids were grown for 3 days. Additional information is provided in Additional file 1 (Supplementary Methods).

Cell culture

Human EGI-1 cholangiocarcinoma cells (German Collection of Microorganisms and Cell Cultures, kind gift from L. Fouassier) were grown in Dulbecco’s Modified Eagle Medium High Glucose with L-Glutamine (DMEM; Lonza/Westburg Leusden, The Netherlands), 10% foetal bovine serum (#F7524, SigmaAldrich) and 1% Penicillin-Streptomycin (Gibco). Cell cultures were incubated at 37ºC in a humidified atmosphere with 5% CO 2 .

Clonogenic assay

Colony forming capacity was determined by seeding 200 cells per well of a 6-well plate. Colony formation was allowed to occur over 10 days under 50 ng/ml treatment of recombinant human protein SEM4A4A (rhSEMA4a; Abcam #ab182683, Amsterdam, The Netherlands) or 10 µg/ml of IgG-SEMA4A previously desalted using a Zeba Spin Desalting Column, with the medium replenished every 3 days. Once colonies had formed, plates were washed in PBS and colonies fixed with methanol and stained 0.5% crystal violet solution (Sigma-Aldrich) for 15 min at room temperature. Following this step, crystal violet was discarded, and plates were washed with water. After drying, plates were scanned, and the number of colonies was analyzed using ImageJ software 1.50 (National Institutes of Health, Bethesda, USA).

Transwell assay

Cell migration was evaluated by using 8.0 μm pore size transwell inserts (Corning) in 24-well plate wells. Prior to migration assessment, cells were pre-treated for 48 h with 50 ng/ml rhSEMA4a or 10 µg/ml of IgG-SEMA4A. Next, cells were seeded into the upper chamber transwell insert (6 × 10 4 cells) in 200 µl serum-free medium while 750 µl medium with 10% foetal bovine serum was added to the lower chambers. In the case of cells treated with rhSEM4A or IgG-SEMA4A, both chambers contained rhSEM4A (50 ng/ml) or IgG-SEMA4A (10 µg/ml), respectively. After 24 h, cells were fixed with methanol and stained with 0.5% crystal violet. Non-migrated cells in the upper chamber were removed using a cotton swab. The area covered by migrated cells was analysed using ImageJ software 1.50 (NIH).

In vivo assessment of semaphorin 4 A function

The impact of IgG-SEMA4A and rhSEMA4A on tumour growth was evaluated in NOD scid gamma (NSG) mice carrying EGI-1 subcutaneous xenografts. 10 6 EGI-1 cells from independent cultures were injected subcutaneously at 4 distinct locations (under the right and left front leg, and under the right and left rear leg) in NSG mice. After 4 weeks, the animals received intraperitoneal injections of PBS, IgG-SEMA4A (5 mg/kg) previously desalted using a Zeba Spin Desalting Column (Thermo Fisher Scientific), or rhSEM4A (500 µg/kg) in a final volume of 200 µl every other day, receiving a total of 4 doses of treatment. Tumour volume (V) was measured every 48 h using a calliper and calculated by the formula [V = 0.5 × (L × W 2 )], where L and W represent the longest and the perpendicular tumour axis respectively. Relative tumour volume was defined by normalising to the initial tumour volume at the start of the treatment (V 0 ).

Data on growth of PBS-treated tumours were obtained with 10 independent EGI-I cell cultures injected in 3 NSG mice (each receiving simultaneous cell injections at 3, 3 and 4 locations, respectively); out of these, 7 tumours were selected for histological analyses and microvascular invasion, and they originated from two mice. Data on growth of rhSEMA4A-treated tumours were obtained with 8 independent EGI-I cell cultures injected in 2 NSG mice (each receiving 4 simultaneous cell injections); out of these, 7 tumours were selected for histological analyses and microvascular invasion. Data on growth of anti-SEMA4A IgG-treated tumours were obtained with 8 independent EGI-I cell cultures injected in 2 NSG mice (each receiving 4 simultaneous cell injections); the 8 tumours were analysed for histology and microvascular invasion.

Bioinformatic analysis of spatial transcriptomic profiling data

Sequencing quality was assessed for each ROI. Raw number of reads ranged from 1,750,000 to 21,875,463. Alignments rates, sequencing saturation and RTSQ30 were respectively higher than 80%, 70%, and 98% in all ROIs. The percent of detected genes (i.e. genes with an expression value higher than the LOQ value, defined as the negative probes geometric means + 2 standard deviations) was evaluated per segment, to identify low-performing AOIs that should be removed. All ROIs were kept, as values ranged from 13.6 to 51.4%. Raw count normalisation and differential expression analyses were performed using DESeq2 Bioconductor package v1.32.0 [ 40 ]. The generalised linear model was fitted using the following design: type of lesion * patient. The lists of differentially expressed genes generated by DESeq2 were ranked on the t-statistic values, and KEGG and HALLMARK gene set enrichment analyses were performed using clusterProfiler v4.0.5 [ 41 ].

Selection of normal epithelium, BilIN and adenocarcinoma in samples of human gallbladder

Our goal is to characterise the spatial transcriptome of gallbladder lesions during progression from normal epithelium to adenocarcinoma. This required gallbladder samples that simultaneously contain non-tumoral (i.e. histologically normal) epithelium, low-grade BilIN, high-grade BilIN and adenocarcinoma, from patients with distinct cancer aetiology. Each lesion must be large enough to enable us to analyse the whole transcriptome of several regions of each type of lesion. Samples that met these critera from two patients were identified in the biobank of the Cliniques Universitaires Saint-Luc: Patient #1 was an 81 year old woman who underwent cholecystectomy to treat cholecystitis; adenocarcinoma was an incidental finding. Patient #2 was a 53 year old man affected with primary sclerosing cholangitis (PSC) whose gallbladder was resected following imaging that revealed a thickening of the gallbladder wall. According to the TNM classification, both patients were staged IIa (pT2a T0 M0). Pathological diagnoses of non-tumoral epithelium, BilINs and adenocarcinoma were made on H&E-stained sections, and were confirmed by two expert pathologists. Patient #1 displayed two small foci of intestinal metaplasia, and no metaplasia was detected in Patient#2. GeoMx Digital Spatial Profiling (NanoString) [ 38 ] was implemented on sections adjacent to the H&E-stained sections to collect whole transcriptome data from 56 epithelial ROIs, each covering approximately 200 epithelial cells of non-tumoral epithelium, BilIN and adenocarcinoma (Table  1 ). Metaplasia in Patient #1 were too small for spatial profiling. Figure  1 illustrates the spatial distribution of areas in which ROIs were delineated (Fig.  1 A), as well as examples of H&E-stained non-tumoral epithelium, BilINs and adenocarcinomas (Fig.  1 B). All adenocarcinoma ROIs in the two patients were well-differentiated (histological grade 1) and compatible with the definition of pancreatico-biliary adenocarcinomas which are characterised by widely spaced tubular structures embedded in a fibrous stroma [ 42 ]. Epithelial ROIs were delineated on sections stained with antibodies which detect markers of the epithelium (panCytokeratin), leukocytes (CD45), and mesenchymal cells (α smooth muscle actin). Nuclei were immunolabeled with anti-Human antigen R antibodies (Additional file 2, Supplementary Fig. S1 ). The ROIs were subjected to transcriptomic analyses as described in Methods.

Selection of non-tumoral (histologically normal) epithelium, BilIN and adenocarcinoma in samples of human gallbladder. ( A ) Low magnification view of gallbladder sections. Squares indicate tissue areas in which several epithelial ROIs were delineated as shown in Additional file 2, Supplementary Fig. S1 . ( B ) Illustrative examples of non-tumoral epithelium, low-grade BilIN, high-grade BilIN and adenocarcinoma. ADC, area containing adenocarcinomas; H&E, haematoxylin-eosin; HG, area containing high-grade BilINs; LG, area containing low-grade BilINs; NT, area containing non-tumoral epithelium

Spatial transcriptomic analysis suggests limited intra-patient variability and distinct modes of tumour progression among the two patients

Principal component analysis (PCA) of the 56 transcriptomes revealed a remarkable clustering of the non-tumoral epithelial samples of the two patients (Fig.  2 A). ROIs from the same type of lesions clustered together within the same patient, but were separated between patients. In Patient #1, adenocarcinoma ROIs clustered close to high-grade BilIN ROIs, whereas adenocarcinomas in Patient #2 appeared closely related to low-grade BilINs. These results were corroborated by the number of differentially expressed genes (log 2 fold change ≥ 1.0; p adj ≤ 0.05) when cross-comparing all tissue types (Additional file 2, Supplementary Fig. S2 A). Together, these data revealed that each lesional type displays limited intra-patient variability, but that distinct mechanisms are driving tumourigenesis in the two patients. Moreover, the PCA plot suggested that adenocarcinoma evolved according to a normal → low-grade BilIN → high-grade BilIN → adenocarcinoma sequence in Patient #1, and according to a normal → low-grade BilIN → adenocarcinoma sequence in Patient #2, with high-grade BilIN emerging separately from adenocarcinoma in this patient.

Distinct modes of tumour progression in two patients revealed by spatial transcriptomic analysis. ( A ) PCA plot of the whole transcriptome of 56 ROIs comprising non-tumoral (histologically normal) epithelia, low-grade biliary BilINs, high-grade BilINs and adenocarcinomas. ( B ) Heatmaps of GSEA enrichment scores comparing adenocarcinoma and non-tumoral epithelium using the KEGG pathway and HALLMARK gene sets (p adj ≤ 0.05). ( C ) Heatmaps of genes from the HALLMARK gene sets that are differentially expressed between adenocarcinoma and normal epithelium ROIs (p adj ≤ 0.05). ADC, adenocarcinoma; HG, high-grade BilIN; LG, low-grade BilIN; NES, normalised enrichment score; NT, non-tumoral epithelium

We next compared the lesions in the two patients and focused on signalling pathways. Using Gene Set Enrichment Analysis (GSEA) [ 43 ], we found several enriched signalling pathways when comparing adenocarcinoma and non-tumoral epithelium. Negative or positive enrichment scores reflect enrichment of downregulated or upregulated genes, respectively (Fig.  2 B). The use of KEGG or HALLMARKS gene sets revealed several pathways that were enriched in both patients, and other pathways that were enriched in only one patient. Heatmaps illustrate genes from the HALLMARKS and KEGG pathway gene sets that are differentially expressed between adenocarcinoma and non-tumoral epithelium in the two patients (Fig.  2 C; Additional file 2, Supplementary Fig. S2 B).

Galbladder cancer is often associated with mutations in PI3KCA , CTNNB1 , KRAS , TP53 , and ERBB2 [ 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 ]. GSEA revealed that PI3K-AKT-mTOR signalling (HALLMARK) is enriched in adenocarcinoma of both patients (Fig.  2 C), and out of the 38 leading edge genes in Patient #2, 23 overlapped with the leading edge genes in Patient#1. HALLMARK gene sets are based on coordinately expressed and biologically relevant genes, and identify pathway activation phenotypes [ 44 ]. Therefore, the positive enrichment of PI3K-AKT-mTOR signalling reflects activation of the pathway. Further, GSEA suggested enrichment of Wnt signalling in both patients, when considering the KEGG Wnt signalling gene set in Patient #1 and the HALLMARK Wnt-β-catenin gene set in Patient #2 (Fig.  2 C). However, the two gene sets differ in their composition, leading to different conclusions in the two patients. In Patient #1, Wnt ligands ( WNT7B , WNT8A , WNT10A , WNT11 ), receptors ( FZD2 , FZD5 ) and effector ( TCF7L2 ) were upregulated in adenocarcinoma as compared to non-tumoral epithelium. Genes induced by Wnt signalling and reflecting activation of a negative feedback loop ( AXIN2 , GSK3B ) further reveal dynamic activity of the Wnt pathway in this patient (Additional file 2, Supplementary Fig. S2 C). In contrast, in Patient #2, only 13 genes from the HALLMARK Wnt-β-catenin gene set were significantly enriched. Among these, most genes are not typical for Wnt signalling and belong to pathways with which Wnt signalling crossreacts. CTNNB1 is upregulated in adenocarcinoma of Patient #2 (log 2 fold change = 1.10; p adj =8.65 × 10 − 10 ), in parallel with upregulation of Wnt signalling inhibitors DKK4 (log 2 fold change = 0.86; p adj =84.76 × 10 − 4 ) and CSNK1E (log 2 fold change = 0.51; p adj =1.66 × 10 − 3 ). Therefore, the analysis of genes of the HALLMARK Wnt-β-catenin gene set does not strongly support that Wnt signalling is active in Patient #2. KRAS signalling differs between the two patients, as evidenced by enrichment of RAS signalling (KEGG) in Patient #1, but downregulation of several KRAS targets within the KRAS signalling up gene set (HALLMARK) in Patient #2 (Fig.  2 C). Similar to KRAS signalling, the p53 pathway differed between patients. Finally, GSEA did not highlight ERBB signalling. However, we found significant overexpression of EGFR, ERBB2 and ERBB3 in Patient #2, but only overexpression of ERBB2 in Patient #1 (Fig.  3 A).

Distinct modes of signalling pathway activation in two patients revealed by spatial transcriptomic analysis. ( A ) Expression of ERBB receptors and ERBB signalling pathway genes during tumour progression. Tables mention the fold change inductions between lesions in the two patients. The corresponding volcano plots are shown, with blue dots highlighting EGFR/ERBB receptors. ( B ) Sequence of enrichment of signalling pathways during tumour progression as determined by GSEA using KEGG pathway and HALLMARK gene sets. Significant enrichments are indicated with p adj values. Red boxes, lesions showing enrichment of the pathway. ns, not significant. ( C ) Differential expression of genes between adenocarcinoma and non-tumoral epithelium in the KEGG pathway Notch. ( D ) Heatmaps of GSEA enrichment scores comparing low grade BilINs and non-tumoral tissues in the two patients. ADC, adenocarcinoma; HG, high-grade BilIN; LG, low-grade BilIN; NES, normalised enrichment score; ns, non-specific; NT-non-tumoral epithelium

Although both patients can display enrichment of the same pathway, we noticed that the sequence of enrichment during tumourigenesis may differ among the patients. Indeed, PI3K-AKT-mTOR signalling became enriched in precursor lesions of Patient #1, namely at the low-grade BilIN → high-grade BilIN transition, whereas it became enriched only at the adenocarcinoma stage in Patient #2 (Fig.  3 B). Other pathways whose enrichment is shared between the patients may in contrast display a similar sequence of enrichment. Indeed, androgen response and estrogen signalling became enriched at the precursor-to-adenocarcinoma transition (Fig.  3 B). Notch signalling was also enriched in adenocarcinoma of both patients, and the enrichment was only significant when comparing non-tumoral epithelium and adenocarcinoma, not when comparing the precursor to adenocarcinoma transitions. This likely reflected a progressive activation throughout the tumourigenic process, without significant jumps between lesional states. Moreover, comparing the expression of leading edge genes in the Notch pathway also revealed interesting differences such as the strong upregulation of NOTCH3 in Patient #1 (log 2 fold change = 2.05; padj = 5.2 × 10 − 11 ) and more modest upregulation of this gene in Patient #2 (log 2 fold change = 0.77; padj = 1.5 × 10 − 3 ) (Fig.  3 C).

Finally, the proposed sequence of tumor progression in patient #2, namely normal → low-grade BilIN → adenocarcinoma, suggests that low-grade BilINs in this patient display upregulated pathways that may be indicative of aggressiveness. In that context, GSEA analyses comparing non-tumoral tissues and low-grade BilINs showed enrichment of the MYC oncogenic pathway in patient #2, unlike in patient #1 (Fig.  3 D). We also noticed that low-grade BilINs of patient #2, unlike those of patient #1, displayed increased expression of Midkine as compared to non-tumoral tissue (log 2 fold change = 2.89; padj: 7.684 × 10 − 15 ). Midkine is known to promote immunosuppressive macrophage differentiation in gallbladder cancer [ 17 ]. Also, EGFR, ERBB2 and ERBB3 expression was upregulated in the low-grade BilINs of patient #2 (Fig.  3 A).

Spatial transcriptomic analysis reveals induction of collagen gene expression in tumoral epithelia

Nepal and coworkers considered the hallmark “epithelial-mesenchymal transition (EMT)” as indicative of poor prognosis [ 26 ]. In Patient #1, the corresponding HALLMARK gene set has the highest enrichment score when comparing adenocarcinoma with non-tumoral epithelium (Fig.  2 B-C). The sequence of EMT enrichment is shown in Fig.  4 A. No similar enrichment was found in Patient #2. Importantly, transcription factors typical for EMT and CADHERINS showed no significant differential expression during tumour progression in either patient (Fig.  4 B). In contrast, extracellular matrix-coding genes were significantly upregulated and contributed significantly to the enrichment of the EMT pathway in the GSEA analyses (Fig.  4 C). To support the latter data at the histological level, we resorted to RNAscope in situ hybridisation. We detected rare mRNAs coding for COL1A1 in non-tumoral epithelia of the two patients. Strong induction of COL1A1 was detected in high-grade BilIN of Patient #1, but also in low-grade BilINs of Patient #2 (Fig.  4 D).

EMT and collagen gene expression during tumour progression. ( A ) Enrichment sequence of EMT (HALLMARK) in Patient #1 demonstrates enrichment throughout tumourigenesis. Significant enrichments are indicated with p adj values. Red boxes, lesions showing enrichment of the pathway. ( B ) Gene expression heatmaps of EMT-promoting transcription factors, and of VIMENTIN and CADHERINS show little or no variation during tumourigenesis. ( C ) Heatmap and volcano plots showing COLLAGEN and LAMININ gene expression in the two patients. Blue dots in volcano plots indicate LAMININ genes. ( D ) RNAscope in situ hybridisation demonstrates induction of COL1A1 mRNA (red dots) starting in high-grade BilINs in Patient #1 and in low-grade BilIN of Patient #2. Tissue sections were immunostained to mark epithelial cells (E-CADHERIN; E-CAD), nuclei (Hoechst), and mesenchymal cells (Smooth muscle protein 22α; SM22α). ADC, adenocarcinoma; HG, high-grade BilIN; LG, low-grade BilIN; NES, normalised enrichment score; NT, non-tumoral epithelium

SEMAPHORIN4A downregulation promotes tumour progression

Our GSEA data uncovered axon guidance signalling as a potential driver of tumour progression in Patient #1 (Fig.  2 B). Axon guidance genes, including SEMAPHORIN/PLEXIN ligand-receptor pairs, were enriched in Patient #1 adenocarcinomas, but not in Patient #2 (Additional file 2, Supplementary Fig. S3 A). SEMA4A was downregulated in the adenocarcinomas of both patients, and this was noticed already at the precursor stages (Fig.  5 A). The involvement of SEMA4A in gallbladder cancer is unexplored, but SEMA4A loss-of-function mutation in familial colorectal cancer type X was found to promote cancer development, thereby revealing a tumour suppressor role for SEMA4A [ 45 , 46 ]. Furthermore, we analysed a public RNAseq dataset from 10 patients for which paired non-tumoral tissue and adenocarcinoma had been collected [ 47 ]. The results showed that SEMA4A was significantly reduced in gallbladder tissue (Additional file 2, Supplementary Fig. S3 B), in line with our findings in patients #1 and #2. This prompted us to investigate the role of SEMA4A in gallbladder cancer development.

SEMA4A displays tumour suppressor properties. ( A ) SEMA4A gene expression is reduced during gallbladder cancer progression. ADC, adenocarcinoma; HG, high-grade BilIN; LG, low-grade BilIN; NES, normalised enrichment score; NT, non-tumoral epithelium; ns, non-specific. ( B ) The epithelium of gallbladder organoids treated with blocking anti-SEMA4A IgG antibody displays focal areas of pseudostratification. This effect was monitored in two experiments out of four. ( C ) rhSEMA4A reduces clonogenicity and transwell migration of cultured EGI-1 cells, whereas anti-SEMA4A IgG antibody had little or no effect. Data show means +/- SEM; n  = 3 or 4; statistical significance was calculated by applying a paired t -test (*, p  < 0.05). ( D ) The histology (Sirius red/fast green staining) and microvascular invasion (MVI) are illustrated in subcutaneous EGI-1 cell tumours following intraperitoneal injection of rhSEMA4A or of blocking IgG SEMA4A antibodies, according to the timing shown in panel E. There is no significant histological difference between the tested conditions, except that rhSEMA4A reduces the number or MVI events in EGI-1 cell tumours, as quantified in the graph. One-way ANOVA was used to compare means (*, p  < 0.05). n  = 7 (control), 7 (IgG SEMA4A) and 8 (rhSEMA4A). ( E ) Growth of subcutaneous EGI-1 cell tumours following intraperitoneal injection of PBS (control), blocking anti-SEMA4A IgG antibody, or rhSEMA4A. n  = 10 (control), 8 (IgG SEMA4A) and 7 (rhSEMA4A). Relative tumour volume and SEM are plotted. Differences between groups were evaluated by performing a two-way Analysis of Variance (two-way repeated measures ANOVA) with Bonferroni correction (*, p  < 0.05; **, p  < 0.01). For further statistical validation, a random intercept-random slope model with continuous time was fitted. This showed a significant interaction between the time and group effect ( p  = 0.03), in particular, the contrast between SEMA4A IgG and control is significant ( p  = 0.048) but not that between control and rhSEMA4A ( p  = 0.95)

We first generated organoids from gallbladder epithelium and selected a line which displayed no karyotypic anomalies. It expressed biliary-specific markers and exhibited biliary transport functions (Additional file 2, Supplementary Fig. S4 ). It also expressed the genes coding for SEMA4A and its receptor Plexin B1 (PLXNB1) (Additional file 2, Supplementary Fig. S3 C). To mimick the downregulation of SEMA4A observed in our transcriptomic analyses, we incubated the organoids for 3 days with a blocking anti-SEMA4A IgG antibody. We found no change in cell proliferation, but observed local areas of pseudostratification of the epithelium in a subset of organoids (Fig.  5 B). The histology of those areas was reminiscent of BilIN, indicating that inhibiting SEMA4A impacts cell polarisation.

We next determined if SEMA4A had additional tumour suppressor properties. Since the organoid lines were not able to induce tumour formation after subcutaneous injection in immunodeficient NSG mice, we used the human extrahepatic cholangiocarcinoma cell line EGI-1. In vitro, clonogenic and transwell migration assays demonstrated that adding rhSEMA4A to cultured EGI-1 cells reduced their clonogenicity and migration (Fig.  5 C). Blocking anti-SEMA4A IgG antibody slightly but not significantly increased colony formation, and did not impact cell migration (Fig.  5 C). In vivo, subcutaneous injection of EGI-1 cells in immunodeficient NSG mice resulted in the formation of tumours. We did not observe any significant histological differences between EGI-1 xenografts treated with intraperitoneal administration of rhSEMA4A, anti-SEMA4A IgG, or the control condition, except for microvascular invasions (Fig.  5 D; Additional file 2, Supplementary Fig. S3 D). Indeed, consistent with the decreased migration induced in vitro by rhSEMA4A, administration of rhSEMA4A resulted in a significant reduction of microvascular invasion in EGI-1 cell-derived tumours (Fig.  5 D). Anti-SEMA4A IgG antibody had no effect on microvascular invasion in the tumours. Recombinant SEMA4A did not impact tumour growth. In contrast, blocking IgG anti-SEMA4A antibody accelerated growth at the earliest stages of tumour growth to progressively reach a plateau (Fig.  5 E). We conclude that SEMA4A can control tumour progression by impacting polarity, clonogenicity and migration of cells.

Earlier mutational profiling of precursor and cancer lesions coexisting in a same patient provided evidence that adenocarcinoma development may be BilIN-dependent or -independent [ 15 ]. Here, using GeoMx technology we extended these findings at the transcriptional level in two patients. We showed that lesions exhibited low intra-patient variability, but exhibited patient-specific sequences of signalling pathway activation.

In Patient #1, ROIs from a same type of lesion were often located at a short distance from each other, except for adenocarcinoma ROIs which were more scattered throughout the tissue sample. In Patient #2, high-grade BilIN ROIs were close to each other, but low-grade BilIN, adenocarcinoma and non-tumoral epithelium ROIs were significantly dispersed (Fig.  1 A). Still, in spite of the scattering within the tissue, the transcriptomic profile of lesions belonging to the same histological type showed low intra-patient variabilty. Such transcriptomic homogeneity likely reflects that cells from a same type of lesion proliferated in a similar environment and with limited accumulation of new mutations. Clonal analysis of gallbladder cancers revealed subclonal diversification [ 48 ], in line with significant epithelial cell heterogeneity in the adenocarcinoma lesions notices in single cell RNA sequencing studies [ 16 , 17 ]. However, our patient samples contained all lesional types on the same tissue sections, suggesting that cancer lesions had not enough time to accumulate genomic lesions, invade the tissue and produce subclones.

The neighbourhood of low-grade BilIN, high-grade BilIN and adenocarcinoma which may occur in pathological samples, leads us to surmise that the epithelium undergoes a normal epithelium → low-grade BilIN → high-grade BilIN → adenocarcinoma histogenic sequence. A contrario , the transcriptomic profile of Patient #2 strongly suggests that adenocarcinoma derived from low-grade BilIN, not from adjacent high-grade BilINs. This contrasted with Patient #1 whose adenocarcinoma ROIs were closely related to high-grade BilINs. We excluded that adenocarcinoma in Patient #2 corresponded to low-grade BilINs extending in Rockitansky-Aschoff sinuses. In Patient #2, only 58 genes were 2-fold up- or downregulated when comparing low-grade BilIN and adenocarcinoma, revealing that low-grade BilIN may be at high risk for evolution towards invasive cancer.

Many signalling pathways were activated during tumour progression and several were common between the two patients. However, the sequence of pathway activation differed between patients, some of the common pathways being activated at the BilIN stage in one patient, but only in the adenocarcinoma cells in the other patient. Therefore, our work suggests that various combinations of pathway activations may end up yielding cancer, no specific pathway or combination of pathways being responsible for transition from one stage to the other.

The HALLMARK gene set “Inflammatory response” was enriched in adenocarcinomas of both patients (not shown), reflecting their common chronic inflammatory background. Still, the tumour aetiology differed in Patients #1 and #2, with Patient #2 being affected with PSC, a disease with high incidence of adenocarcinoma [ 49 ]. The adenocarcinoma in Patient#2 was mucosecreting (Fig.  1 B), unlike the carcinoma in Patient #1. The mutational profile of cholangiocarcinoma in PSC is heterogeneous and affects genes similar to those in non-PSC associated cholangiocarcinoma, the most frequently mutated being TP53 , KRAS , PI3KCA and GNAS . In low-grade and high-grade dysplastic lesions, loss or amplifications of several genes, as well as mutations in ERBB2 and TP53 , can already occur [ 50 , 51 ]. Our work extend these data at the transcriptomic level and highlight that low-grade BilIN can be very closely related to adenocarcinoma.

EMT is a phenotypic continuum during which epithelial cells evolve to a mesenchymal state via transitional or hybrid states [ 52 ]. It involves disruption of polarity and intercellular adhesion, changes in the interaction between cells and extracellular matrix, and increased migration [ 53 , 54 ]. Interestingly, both patients display increased expression of COL1A1 and COL1A2 . This differs from pancreatic cancer in which COL1A2 is no longer expressed [ 55 ], leading to the production of collagen α1/α1/α1 trimers which promote tumor progression.

SEMA4A is a tumour suppressor in colorectal cancer [ 45 , 46 ]. Here we found that it is downregulated in both patients during gallbladder tumour progression, starting at the BilIN stage. Downregulation of SEMA4A in gallbladder cancer was also found in other patients (Additional file 2, Supplementary Fig. S3 B). Gallbladder organoids expressed SEMA4A and its receptor PLXNB1 and the levels of SEMA4A expression varied considerably (Additional file 2, Supplementary Fig. S3 B), likely explaining the variable pseudostratification of the gallbladder organoids when treated with blocking IgG antibody (Fig.  5 B). Also, the low levels of SEMA4A and PLXNB1 in cholangiocarcinoma EGI-1 cells, as compared to organoids derived from normal gallbladder epithelium, fit with the notion that SEMA4A is repressed in biliary cancer cells and with our observation that anti-SEMA4 blocking antibodies have limited or no effect on clonogenicity and migration of EGI-1 cells in vitro. In vivo, we detected a higher level of SEMA4A in EGI-1 cell-drived tumours than in in vitro cultured EGI-1 cells (Additional file 2, Supplementary Fig. S3 C). We excluded that this results from SEMA4A production by tumour-invading mouse cells, as our PCR primers were designed to specifically detect human SEMA4A. Inhibiting this in vivo production of SEMA4A enabled us to monitor growth-promoting properties of anti-SEMA4 blocking antibodies. How these anti-SEMA4A antibodies promote EGI-1 cell-derived tumour growth remains unclear. Indeed, our data show that inhibiting SEMA4A accelerates tumour growth during 4 days. This effect slows down to reach a plateau (Fig.  5 E), and at the plateau stage we noticed a slight but not significant increase in proliferation rate, as evidenced by immunostaining for phospho-Histone H3 (Additional file 2, Supplementary Fig. S3 C). We hypothesise that anti-SEMA4A antibodies promoted proliferation mainly during the first 4 days of treatment. Interestingly, rhSEMA4 did not impact tumour growth, but decreased microvascular invasion, suggesting that reduction of SEMA4 promotes metastasis. The signalling pathways mediating the effects of SEMA4A on migration, polarity and potentially proliferation deserve further investigation. Further studies will determine how frequently SEMA4A is repressed at early stages of gallbladder cancer and whether understanding its pathway may lead to identify biomarkers of early diagnosis of gallbladder tumours.

Our spatial transcriptomic analysis reveals that precursor and cancer lesions can display limited intra-patient variability during gallbladder cancer progression and supports that tumourigenic mechanisms are patient-specific. Repression of SEMA4A may contribute to tumour progression. Our work also underscores that low-grade BilINs may be at high risk for developing to cancer and should ideally be characterised by gene expression profiling.

Data availability

The dataset supporting the conclusions of this article is available in Gene Expression Omnibus (GSE259311).

Abbreviations

• Biliary intraepithelial neoplasia

Epithelial-mesenchymal transition

Formalin-fixed paraffin-embedded

Haematoxylin and eosin

Primary sclerosing cholangitis

Region of interest

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Acknowledgements

The authors thank Cédric Van Marcke de Lummen (Université Catholique de Louvain, Brussels, Belgium) for advice; Atsushi Kumanogoh (Osaka University, Osaka, Japan), Thomas Worzfeld (University of Marburg, Marburg, Germany) and Svetlana Chapoval, University of Maryland, Baltimore, MA, USA) for information on SEMA4A biology; the Lemaigre lab members for help and support.

The work of F.P.L. was supported by the Belgian Foundation against Cancer (grant #2018-078), and the Fonds Joseph Maisin (grants 2020–2021 and 2022–2023). F.P.L. and L.G. were supported by the Fonds de la Recherche Scientifique (F.R.S.-F.N.R.S. Belgium, grant Télévie #7.8505.21). S.P., F.M.-N. and A.L. were supported by fellowships from the Fonds de la Recherche Scientifique (grants Télévie #7.4544.18 and Télévie #7.6510.20 to S.P.; Télévie #7.8505.21 to F.M.-N. and A.L.).

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Sophie Pirenne

Present address: Department of Imaging & Pathology, UZ Herestraat 49, Leuven, 3000, Belgium

Sophie Pirenne and Fátima Manzano-Núñez contributed equally to this work.

Authors and Affiliations

de Duve Institute, Université catholique de Louvain, Avenue Hippocrate 75, Brussels, B1-7503, 1200, Belgium

Sophie Pirenne, Fátima Manzano-Núñez, Axelle Loriot, Sabine Cordi, Nisha Limaye, Laurent Gatto & Frédéric P. Lemaigre

Support en Méthodologie et Calcul Statistique, Université catholique de Louvain, Voie du Roman Pays 20, Louvain-la-Neuve, 1348, Belgium

Lieven Desmet

Institut de Recherche Expérimentale et Clinique, Université Catholique de Louvain, Avenue Hippocrate 75, Brussels, 1200, Belgium

Selda Aydin & Catherine Hubert

Department of Pathology, Cliniques universitaires Saint-Luc, Avenue Hippocrate 10, Brussels, 1200, Belgium

Selda Aydin

Department of Medical Oncology, Cliniques universitaires Saint-Luc, Avenue Hippocrate 10, Brussels, 1200, Belgium

Catherine Hubert

Institut de Pathologie et de Génétique, Avenue Georges Lemaître 25, Charleroi, 6041, Belgium

Sébastien Toffoli

Institute of Pathology, Lausanne University Hospital CHUV, University of Lausanne, Rue du Bugnon 25, Lausanne, 1011, Switzerland

Christine Sempoux

Department of Pathology, School of Medicine, International University of Health and Welfare, Narita Hospital, Narita, Japan

Mina Komuta

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Contributions

S.P., F.M.-N., A.L., M.K., L.G. and F.L. designed the study; S.P., F.M.-N., A.L., S.C., S.A., C.H., S.T., acquired data; S.P., F.M.-N., A.L., L.D., N.L., C.S., M.K., L.G. and F.L. analysed and interpreted data. S.P., F.M.-N., A.L., L.D., N.L., L.G. performed statistical analyses. S.P., F.M.-N. and F.L. drafted the manuscript. All authors read and approved the final paper.

Corresponding author

Correspondence to Frédéric P. Lemaigre .

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The study on human samples was conducted in compliance with the ethical guidelines of the 2013 Declaration of Helsinki and was approved by the Comité d’Ethique Hospitalo-Facultaire (UCLouvain and Cliniques Universitaires Saint-Luc) with numbers 2018/06Jul/281 and 2021/26OCT/444. In accordance with article 8 of the internal rules of the Cliniques universitaires Saint-Luc, the need for informed consent was waived to the present retrospective study. The study is based solely on the analysis of residual human body material and on the collection of data existing in the medical files of patients who have not expressed their opposition to the use of their medical file for scientific research purposes. An informed consent exemption request was thus presented to the Ethics Committee, which was accepted. Mice received humane care and the research protocol was approved by the Animal Welfare Committee of the Université Catholique de Louvain with number 2022/UCL/MD/17.

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Pirenne, S., Manzano-Núñez, F., Loriot, A. et al. Spatial transcriptomics profiling of gallbladder adenocarcinoma: a detailed two-case study of progression from precursor lesions to cancer. BMC Cancer 24 , 1025 (2024). https://doi.org/10.1186/s12885-024-12770-0

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InformedHealth.org [Internet]. Cologne, Germany: Institute for Quality and Efficiency in Health Care (IQWiG); 2006-.

InformedHealth.org [Internet].

In brief: what types of studies are there.

Last Update: September 8, 2016 ; Next update: 2024.

There are various types of scientific studies such as experiments and comparative analyses, observational studies, surveys, or interviews. The choice of study type will mainly depend on the research question being asked.

When making decisions, patients and doctors need reliable answers to a number of questions. Depending on the medical condition and patient's personal situation, the following questions may be asked:

• What is the cause of the condition?
• What is the natural course of the disease if left untreated?
• What will change because of the treatment?
• How many other people have the same condition?
• How do other people cope with it?

Each of these questions can best be answered by a different type of study.

In order to get reliable results, a study has to be carefully planned right from the start. One thing that is especially important to consider is which type of study is best suited to the research question. A study protocol should be written and complete documentation of the study's process should also be done. This is vital in order for other scientists to be able to reproduce and check the results afterwards.

The main types of studies are randomized controlled trials (RCTs), cohort studies, case-control studies and qualitative studies.

• Randomized controlled trials

If you want to know how effective a treatment or diagnostic test is, randomized trials provide the most reliable answers. Because the effect of the treatment is often compared with "no treatment" (or a different treatment), they can also show what happens if you opt to not have the treatment or diagnostic test.

When planning this type of study, a research question is stipulated first. This involves deciding what exactly should be tested and in what group of people. In order to be able to reliably assess how effective the treatment is, the following things also need to be determined before the study is started:

• How long the study should last
• How many participants are needed
• How the effect of the treatment should be measured

For instance, a medication used to treat menopause symptoms needs to be tested on a different group of people than a flu medicine. And a study on treatment for a stuffy nose may be much shorter than a study on a drug taken to prevent strokes .

“Randomized” means divided into groups by chance. In RCTs participants are randomly assigned to one of two or more groups. Then one group receives the new drug A, for example, while the other group receives the conventional drug B or a placebo (dummy drug). Things like the appearance and taste of the drug and the placebo should be as similar as possible. Ideally, the assignment to the various groups is done "double blinded," meaning that neither the participants nor their doctors know who is in which group.

The assignment to groups has to be random in order to make sure that only the effects of the medications are compared, and no other factors influence the results. If doctors decided themselves which patients should receive which treatment, they might – for instance – give the more promising drug to patients who have better chances of recovery. This would distort the results. Random allocation ensures that differences between the results of the two groups at the end of the study are actually due to the treatment and not something else.

Randomized controlled trials provide the best results when trying to find out if there is a cause-and-effect relationship. RCTs can answer questions such as these:

• Is the new drug A better than the standard treatment for medical condition X?
• Does regular physical activity speed up recovery after a slipped disk when compared to passive waiting?
• Cohort studies

A cohort is a group of people who are observed frequently over a period of many years – for instance, to determine how often a certain disease occurs. In a cohort study, two (or more) groups that are exposed to different things are compared with each other: For example, one group might smoke while the other doesn't. Or one group may be exposed to a hazardous substance at work, while the comparison group isn't. The researchers then observe how the health of the people in both groups develops over the course of several years, whether they become ill, and how many of them pass away. Cohort studies often include people who are healthy at the start of the study. Cohort studies can have a prospective (forward-looking) design or a retrospective (backward-looking) design. In a prospective study, the result that the researchers are interested in (such as a specific illness) has not yet occurred by the time the study starts. But the outcomes that they want to measure and other possible influential factors can be precisely defined beforehand. In a retrospective study, the result (the illness) has already occurred before the study starts, and the researchers look at the patient's history to find risk factors.

Cohort studies are especially useful if you want to find out how common a medical condition is and which factors increase the risk of developing it. They can answer questions such as:

• How does high blood pressure affect heart health?
• Does smoking increase your risk of lung cancer?

For example, one famous long-term cohort study observed a group of 40,000 British doctors, many of whom smoked. It tracked how many doctors died over the years, and what they died of. The study showed that smoking caused a lot of deaths, and that people who smoked more were more likely to get ill and die.

• Case-control studies

Case-control studies compare people who have a certain medical condition with people who do not have the medical condition, but who are otherwise as similar as possible, for example in terms of their sex and age. Then the two groups are interviewed, or their medical files are analyzed, to find anything that might be risk factors for the disease. So case-control studies are generally retrospective.

Case-control studies are one way to gain knowledge about rare diseases. They are also not as expensive or time-consuming as RCTs or cohort studies. But it is often difficult to tell which people are the most similar to each other and should therefore be compared with each other. Because the researchers usually ask about past events, they are dependent on the participants’ memories. But the people they interview might no longer remember whether they were, for instance, exposed to certain risk factors in the past.

Still, case-control studies can help to investigate the causes of a specific disease, and answer questions like these:

• Do HPV infections increase the risk of cervical cancer ?
• Is the risk of sudden infant death syndrome (“cot death”) increased by parents smoking at home?

Cohort studies and case-control studies are types of "observational studies."

• Cross-sectional studies

Many people will be familiar with this kind of study. The classic type of cross-sectional study is the survey: A representative group of people – usually a random sample – are interviewed or examined in order to find out their opinions or facts. Because this data is collected only once, cross-sectional studies are relatively quick and inexpensive. They can provide information on things like the prevalence of a particular disease (how common it is). But they can't tell us anything about the cause of a disease or what the best treatment might be.

Cross-sectional studies can answer questions such as these:

• How tall are German men and women at age 20?
• How many people have cancer screening?
• Qualitative studies

This type of study helps us understand, for instance, what it is like for people to live with a certain disease. Unlike other kinds of research, qualitative research does not rely on numbers and data. Instead, it is based on information collected by talking to people who have a particular medical condition and people close to them. Written documents and observations are used too. The information that is obtained is then analyzed and interpreted using a number of methods.

Qualitative studies can answer questions such as these:

• How do women experience a Cesarean section?
• What aspects of treatment are especially important to men who have prostate cancer ?
• How reliable are the different types of studies?

Each type of study has its advantages and disadvantages. It is always important to find out the following: Did the researchers select a study type that will actually allow them to find the answers they are looking for? You can’t use a survey to find out what is causing a particular disease, for instance.

It is really only possible to draw reliable conclusions about cause and effect by using randomized controlled trials. Other types of studies usually only allow us to establish correlations (relationships where it isn’t clear whether one thing is causing the other). For instance, data from a cohort study may show that people who eat more red meat develop bowel cancer more often than people who don't. This might suggest that eating red meat can increase your risk of getting bowel cancer. But people who eat a lot of red meat might also smoke more, drink more alcohol, or tend to be overweight. The influence of these and other possible risk factors can only be determined by comparing two equal-sized groups made up of randomly assigned participants.

That is why randomized controlled trials are usually the only suitable way to find out how effective a treatment is. Systematic reviews, which summarize multiple RCTs , are even better. In order to be good-quality, though, all studies and systematic reviews need to be designed properly and eliminate as many potential sources of error as possible.

• German Network for Evidence-based Medicine. Glossar: Qualitative Forschung.  Berlin: DNEbM; 2011. 
• Greenhalgh T. Einführung in die Evidence-based Medicine: kritische Beurteilung klinischer Studien als Basis einer rationalen Medizin. Bern: Huber; 2003. 
• Institute for Quality and Efficiency in Health Care (IQWiG, Germany). General methods . Version 5.0. Cologne: IQWiG; 2017.
• Klug SJ, Bender R, Blettner M, Lange S. Wichtige epidemiologische Studientypen. Dtsch Med Wochenschr 2007; 132:e45-e47. [ PubMed : 17530597 ]
• Schäfer T. Kritische Bewertung von Studien zur Ätiologie. In: Kunz R, Ollenschläger G, Raspe H, Jonitz G, Donner-Banzhoff N (eds.). Lehrbuch evidenzbasierte Medizin in Klinik und Praxis. Cologne: Deutscher Ärzte-Verlag; 2007.

IQWiG health information is written with the aim of helping people understand the advantages and disadvantages of the main treatment options and health care services.

Because IQWiG is a German institute, some of the information provided here is specific to the German health care system. The suitability of any of the described options in an individual case can be determined by talking to a doctor. informedhealth.org can provide support for talks with doctors and other medical professionals, but cannot replace them. We do not offer individual consultations.

Our information is based on the results of good-quality studies. It is written by a team of health care professionals, scientists and editors, and reviewed by external experts. You can find a detailed description of how our health information is produced and updated in our methods.

• Cite this Page InformedHealth.org [Internet]. Cologne, Germany: Institute for Quality and Efficiency in Health Care (IQWiG); 2006-. In brief: What types of studies are there? [Updated 2016 Sep 8].

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