Study designs


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Study designs


Evaluation of diagnostic tests

Sensitivity and specificity

Positive and negative predictive value

Test precision and accuracy

Epidemiological measurements

Incidence and prevalence

Relative and absolute risk

Odds ratio

Attributable risk (AR)

Mortality rates and case-fatality


Direct standardization

Indirect standardization

Study design

Study designs

Ecologic study

Cross sectional study

Case-control study

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Randomized control trial

Clinical trials

Sample size

Placebo effect and masking

Causation, validity and bias

Disease causality

Selection bias

Information bias



Bias in interpreting results of clinical studies

Bias in performing clinical studies

Public health

Modes of infectious disease transmission

Outbreak investigations

Disease surveillance

Vaccination and herd immunity



Study designs

USMLE® Step 1 questions

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USMLE® Step 2 questions

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USMLE® Step 1 style questions USMLE

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USMLE® Step 2 style questions USMLE

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A researcher is attempting to better understand correlational risk factors for hyperlipidemia. He obtains data on approximately 30,000 patients in a small city in the United States and finds a higher prevalence of hyperlipidemia in patients with a high meat intake when compared to those who identify as vegetarians. Which of the following best describes this type of study design?  


Content Reviewers

Rishi Desai, MD, MPH


Evan Debevec-McKenney

Pauline Rowsome, BSc (Hons)

There are six basic types of epidemiological study designs, and they can each be distinguished using certain criteria.

The first criterion for deciding which study design to use is whether you have individual or group data.

For example, let’s say we want to know how many people out of 100 people had migraines in the past year.

Now, with individual data, we have information about each person, so we can tell whether or not each of the 100 people had a migraine.

So, let’s say that 9 people had migraines. If we have individual data, we can look at the individual characteristics for each of the 9 people that had migraines, like their sex, age, race, or past history of migraines, and we can compare them to the people that didn’t have migraines.

On the other hand, if we have group data, we don’t actually know which specific individuals out of the 100 people had migraines.

So even though we know that 9 people had them, we don’t know which 9 people they were or any of their individual characteristics.

Now, ecological studies are a type of study design that uses group data to figure out if there is a potential association between two variables.

For example, let’s say you want to figure out if people who sleep less are more likely to get migraines.

And perhaps you have information about average sleep duration for populations in ten different cities.

You could plot this information on a graph with average sleep duration on the x-axis and the prevalence of migraines—which is the number of people that suffer from migraines, per 100,000 people—on the y-axis.

Generally, we can see that the less sleep a city gets, the higher the prevalence of migraines is for that city.

The thing is, we can’t actually say that getting less sleep causes migraines, since we don’t have information about each individual in each city.

All we can say is that there’s an association between sleep duration and prevalence of migraines.

Ecological studies are helpful for making hypotheses, though, that can later be tested using individual-level studies.

And, in general, individual-level studies are considered stronger than ecological studies, because knowing individual characteristics can help us determine what risk factors are associated with certain diseases.



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