Transcript for Study designs
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.
So now let’s talk about studies that use individual data.
The next criterion we use to decide on a study design is whether or not there’s an intervention, and an intervention is basically just an exposure that the researcher controls.
Studies with interventions are also called experimental studies, randomized controlled trials, or RCTs for short.
So, for example, let’s say we want to find out if a newly discovered drug, we’ll call it Drug A, can prevent migraines for up to a year. In this example, Drug A is the intervention and having a migraine is the outcome.
In the most basic RCT, the sample population might be randomly split into two treatment groups, an intervention group that receives Drug A, and a control group that receives a placebo.
The placebo looks and tastes like Drug A but is completely harmless and ineffective - like a tiny capsule filled with water.
After both groups get their treatments, researchers would compare the incidence of migraines in each group—which is the number of individuals in each group who got migraines over the next year.
Now, RCTs are considered to be the gold standard study design because they’re able to determine causality.
In other words, we can determine if taking Drug A causes people to have fewer migraines compared to taking the placebo.
Determining causality is possible because the intervention group and the control group are randomly selected from the larger target population, so there’s a good chance that people in each group are similar and that the only difference between the two groups is whether or not they were exposed to Drug A.
There are some downsides to RCTs though, mainly that they can sometimes be really expensive, time consuming, and in some cases unethical, depending on the intervention.
Next, let’s talk about studies that don’t have an intervention, and these are called observational studies, because you simply observe what happens to individuals without controlling their exposure.
There are a few different types of observational studies, and the main criterion used to distinguish them is when you measure the exposure.
In other words, whether you measure the exposure before the outcome, after the outcome, or at the same time.
The first observational study is cohort or longitudinal studies, and cohort studies measure the exposure before the outcome.
Now, a cohort is simply a group of people who share a common characteristic.
So, cohort studies are a type of study design that look at individuals in a cohort who have a certain exposure, as well as individuals in a cohort who have not had that exposure, and then follow both groups over time and compare the incidence of a certain outcome.
For example, let’s say we follow a group of 100 individuals that smoke cigarettes, the exposed group, and 100 people that don’t smoke cigarettes, the unexposed group, and compare the incidence of migraines during the next five years.
Cohort studies are useful when you want to show the timing or temporality of the relationship between the exposure and the outcome.
For example, out of 200 people, 100 that smoke and 100 that don’t smoke, none of them have migraines at the beginning of the study.
But after five years, more individuals that smoke have migraines compared to individuals that don’t smoke, so it’s pretty clear that smoking happened first and migraines happened second.
Cohort studies are also good for looking at rare exposures, like if a certain uncommon medication causes an increased risk of migraines.
It makes more sense to recruit 100 people who were all already using the uncommon medication rather than start with 100 people with migraines and try to figure out if any of them had been exposed to the medication in the past; because since it’s such a rare exposure, there might be only 1 or 2 people out of the 100 individuals with migraines who were exposed to the medication.
A third reason you’d use cohort studies is to look at multiple outcomes from one specific exposure.