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Randomized control trial

Randomized control trial


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High Yield Notes
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Randomized control trial

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Content Reviewers:

Rishi Desai, MD, MPH

Randomized controlled trials or RCTs are a type of study design that’s often used to figure out if there’s a causal relationship between an exposure and an outcome.

For example, let’s say we want to find out if a newly discovered drug, let’s call it Drug A, can prevent migraines for up to a year.

In this example, Drug A is the exposure and having a migraine is the outcome.

In the most basic randomized controlled trial, the sample population might be randomly split into two treatment groups, an exposure 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 number of individuals in each group who got migraines over the next year.

Typically, the goal of a randomized controlled trial is to figure out if the intervention can help some target population - usually that’s just people in the general population.

That means that it’s important to perform the trial on individuals that accurately represent the general population.

In other words, the sample population should be similar to the general population.

For example, if researchers want to find out if Drug A helps prevent migraines in women, then women are the target population.

And the randomized control trial should be done on women rather than being done on men.

Furthermore, if the goal is to use Drug A for women around the world, then the sample population shouldn’t just include women living in Vancouver, British Columbia.

Instead it should include women of all ages, races, and socioeconomic statuses from around the world.

If the sample population and that target population are really similar, then the randomized controlled trial has high external validity, meaning that any conclusions made about the sample population can be applied to the target population, which is good news for the manufacturer of Drug A and hopefully means fewer headaches for women around the world!

Now, to make the sample population represents the target population, one tool that can be used is randomization, meaning that individuals get selected to enter the study through a process of chance.

To show how that works, let’s say they put the names of every women in the target population into a brown paper bag, a big brown paper bag.

So that’s 3.5 billion names in that bag - probably with a number of repeats.

Then let’s say that you choose a thousand names out of the bag to include in the study - either by simply picking them or by using a computer program to make sure that it’s truly by chance.

That’s randomization.

Randomization minimizes the chance of selection bias, which is when researchers decide for themselves who to include in a research study.

For example, the researchers might choose slightly older women with a median age of 60, simply because they have more time and are more willing to join the study.

Now, as it turns out the median age of a woman around the world is 30.

So, in this example, the study results wouldn’t necessarily apply to the target population, and the study would have low external validity.

After figuring out who will participate in the study - the sample population - the next step is to divide the participants into treatment groups.

This is done by random allocation, where an individual is assigned to a treatment group once again by chance.

The goal is to make sure that the treatment groups - whether there are two like in our study or many different treatment groups - all have similar characteristics.