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Bias in interpreting results of clinical studies
Bias in performing clinical studies
Attributable risk (AR)
DALY and QALY
Incidence and prevalence
Mortality rates and case-fatality
Relative and absolute risk
Positive and negative predictive value
Sensitivity and specificity
Test precision and accuracy
Modes of infectious disease transmission
Vaccination and herd immunity
Cross sectional study
Placebo effect and masking
Randomized control trial
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A confounder is a variable in a study that distorts the true relationship between an exposure and an outcome, so it looks like the exposure and the outcome are either more associated or less associated than they really are.
For example, let’s say you hear on the news that drinking coffee is associated with developing heart disease, and - because you drink a lot of coffee - you decide to conduct a study to see if this is true.
First, you recruit 100 people that drink coffee and 100 people that don’t drink coffee, follow them for ten years, and then compare the number of people who developed heart disease in each group.
First, off you must really love coffee and be fairly wealthy to spend ten years studying it at the drop of a hat.
Now, let’s say that the proportion of people who develop heart disease in the coffee drinking group is - 50 out of 100, or 50% - and proportion of people who develop heart disease in the non-coffee drinking group - is 20 out of 100, or 20%.
Comparing 50% and 20%, you get a relative risk of 2.5, meaning the risk of developing heart disease for people that drink coffee is 2.5 times the risk for people that don’t drink coffee.
The association between coffee drinking and heart disease can be represented by an arrow pointing from the exposure to the outcome.
The arrow represents a potential causal relationship - in other words, coffee drinking potentially causes the development of heart disease.
But does drinking coffee really cause heart disease? Maybe, or maybe there’s a mysterious third variable - like smoking - that’s confounding the relationship, or making it look like there’s an association when there really isn’t one.
To be considered a confounder, two conditions have to be met.
The first condition is that a variable has to be associated with the exposure - meaning that the variable is seen to occur significantly more frequently among one group than the other.
So in this case, people that smoke would have to be either more or less likely to drink coffee compared to people that don’t smoke.
For example, 45 people - or 45% - in the coffee drinking group smoked compared to 5 people - or 5% - in the non-coffee drinking group, so people that smoked are 9 times more likely to drink coffee than people that don’t smoke.
A confounding variable is a variable that distorts the accurate relationship between exposure and the outcome. For example, suppose you're studying the effects of a new drug. In that case, age might be a confounding variable because the drug may affect people of different ages differently. Confounders can make the outcome and exposure look more or less than they are.
There are many ways to control for confounding variables, such as stratifying your data (i.e., dividing your data into subgroups) or using multivariate analysis. However, there is no perfect way to completely eliminate confounding variables, so always be aware of them and try to account for them as best you can.
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