Types and Designs of Clinical Studies

Introduction Clinical study types and designs are terms which represent the way in which clinical trials are structured and formulated.

Since we all know that clinical research is an extremely complex topic and not everything can be explained in a simple way, here we’ll focus only on some of the most basic types of clinical study types and designs which involve human subjects or participants.

First of all, you should know that the most basic grouping of study designs is experimental (treatment) studies and observational studies.

As we can suppose from the names, in an observational study, researchers have less control over subjects and they’re just observing what happens to subjects, while in experimental studies, researchers are using different methods (such as randomization) to place subjects in separate groups. This gives experimental studies much more validity than observational studies.

In this guide, we’ll talk about the 2 possible types of studies, as well as different study designs within.

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Types of Studies: Parallel and Crossover

Part I

Parallel Study

For a better and simplified explanation, let’s imagine we have two groups of patients - GROUP 1 and GROUP 2, and two different

treatments - TREATMENT A and TREATMENT B.

In a parallel study, the structure is as follows:

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Treatment A

Treatment B

Group 1

Group 2

Parallel Study

Throughout the whole duration of the study, GROUP 1 will receive only TREATMENT A, and GROUP 2 will receive only TREATMENT B! None of the groups will receive any other treatment but the one they were given at

the beginning of the study!

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TREATMENT A and TREATMENT B can either be two different active drugs, or one active drug and one placebo, or even two different doses of the same drug. In any case, if the first group started with placebo, they will continue with only placebo and nothing else! Parallel studies are also called non-crossover studies.

- progressive diseases;- life-threatening conditions;- acute conditions;- ‘’Carry-over’’ treatments;

- harder to enroll;- needs more patients;

Crossover Study

For a better and simplified explanation, let’s imagine we have two groups of patients - GROUP 1 and GROUP 2, and two different

treatments - TREATMENT A and TREATMENT B.

In a crossover study, the structure is as follows:

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Treatment A

Treatment B

Group 1

Group 2

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Crossover Study

Contrary to parallel studies, in a crossover study, GROUP 1 might start with TREATMENT A but then at a certain point be given TREATMENT B,

while GROUP 2 might start with TREATMENT B but then be given TREATMENT A.

In between two different treatments, there’s usually a so-called wash-out period, in order to avoid a ‘’carryover effect’’.

So, in crossover studies, a study subject (participant) will be given a number of different treatments (at least two). That’s why it’s called a crossover study - GROUP 1 that starts with TREATMENT A crosses to TREATMENT B. This means that each subject will serve as both the treatment and the control group!

Concerning the IP (investigational product or drug), the same applies as with parallel studies. One treatment might be the actual active drug while the other can be a placebo, and there can also be a third treatment (another active drug that will serve for comparison).

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Crossover Study

Crossover studies are beneficial for some conditions while not suitable for others. Below, you’ll be able to see the benefits and warnings of crossover studies.

- chronic conditions;- controlled conditions;- non-life-threatening conditions;- Phase I and II Studies;- easier to enroll;- needs less patients;

- progressive diseases;- life-threatening conditions;- acute conditions;

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Study Designs

Part II

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Study Designs

Meta-analysis

Randomized controlled trial (RCT)

Cohort study

Case-control study

Case series

Cross-sectional 10

Designs according to their data validity, from

the lowest up to the highest

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Meta-analysis

This type of study is conducted with the aim to gain a better and closer estimate to the reality. We all know that every study design can have a certain error, which is why meta-analysis combines these multiple studies together, in order to draw a better and more accurate conclusion.

Even though a meta-analysis can easily be ranked the highest on a validity scale, this doesn’t mean that there isn’t any space for errors. After all, meta-analysis combines multiple studies together, so if those studies have a lot of errors individually, then the meta-analysis will also have errors.

Meta-analysis is actually a statistical process which takes and combines the results of various different individual studies in order to reach a

single conclusion.

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Randomized-controlled trial (RCT)RCT is nowadays the ‘’gold standard’’ of experimental (treatment) studies. RCTs are considered to have high validity because of the fact that they’re experimental and controlled studies which means that it’s much easier to isolate separate circumstances and come to a better (more valid) conclusion. These studies include randomization and double-blind, which gives even more validity to the RCT study design.

The process of randomization which makes these studies so good means to randomly place subjects to one of two (or more) groups. One of these groups will receive the IP (investigational product), while the other will receive placebo (or a different mainstream treatment). This process minimizes selection bias. The point of such studies is to compare the outcomes and results of the IP group with the control group.

Experimental

Controlled

Randomized

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Cohort Study

A cohort study is basically following a group of similar people that have certain differing factors. These people will all have similar features, but some of them will have exposure to certain factors (or be affected by a condition) and some won’t. So researchers will follow this group of people over time and then compare the results and determine how certain factors affected the subjects.

Cohort studies can be prospective (what we explained above) and retrospective - the opposite. In retrospective cohort studies, researchers compare the past records of two similar groups of people who differ by some characteristics.

An example for a cohort study would be a group of female doctors, half of whom are smokers and the other half non-smokers, or a group of middle-aged truck drivers, some of which are smokers and some non-smokers.

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Case-control Study

Case-control studies are the opposite of cohort studies. In this type of studies, the researchers begin with the outcome. This means that first, they find a group of participants affected by a certain condition and next, they try to find a similar group of people that share the same exposures and features with the first group but are not affected by the condition (control group).

To sum up, both groups have had the same exposure, but one group was affected by it while the other one wasn’t. As we can see, case-control studies are retrospective studies.

Exposure

NO SIDE EFFECTS

SIDE EFFECTS

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Case-series

A case series study design is again an observational study but of a really small size that doesn’t entail a control group.

Basically, a case series is only reviewing the medical records of a small number of individuals. This is all done in order to see the outcomes of these patients. A case series study ranks low on the validity scale.

REVIEWING MEDICAL RECORDS

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Cross-sectional

DETERMINING PREVALENCEIn a cross-sectional study, researchers are trying to evaluate and measure the prevalence of a specific exposure versus the prevalence of a specific health outcome simultaneously (at the same time). In other words, they’re comparing the number of people who are exposed with the number of people who are affected by that exposure at one specific point. Even though this study is easy to do and it doesn’t require a lot of resources, its validity is questioned because of all the other factors that are involved. With this type of study, it’s really hard to connect the exposure as the primary culprit of a health condition.

Even though these studies have a huge space for errors, they can be useful in a more general situation.

For example, it can be easy to notice a city with huge prevalence of the risk factor which also has a huge prevalence of people affected by the expected health outcome. In this case, we can clearly see the connection between the risk factor (exposure) and the health outcome.

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CONCLUSION

As a final word, all of these clinical study types and designs have their own place in the clinical research world. Even though one study design might not possess enough accuracy for a certain trial, it might be good for another type of trial. The way in which study types and designs are chosen depends on the nature of the study as well as the desired outcomes. Finally, we hope that we’ve managed to help you understand the difference between a parallel and a crossover study, and gave you enough information to differentiate study designs in a clear and simplified way.

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