What is Confounding by Indication?
Confounding by indication is likely to happen when a particular medicine is linked to the outcome of interest in a study. For example, let’s say you’re observational study is looking into the effects of a new drug A on outcomes for patients with cardiovascular disease (CVD). As the study is observational, there’s no control group or experimental group, and you’re merely observing what happens. In this particular example, patients with more severe cases of CVD are more likely to be prescribed drug A, but they are also more likely to have adverse events (e.g. a stroke). Therefore, your study might conclude that drug A isn’t very effective, because it appears patients have more severe events. These misleading effects (or lack of effects) are what confounding by indication is all about.
Confounding by contraindication is, despite the similar sounding name, completely different from confounding by indication. O. Miettinen, in Epidemiological Research: Terms and Concepts, notes that it’s very rare to find confounding by contraindication in a study, while confounding by indication is quite common.
Controlling for Confounding by Indication
Confounding by indication is difficult to control for. One of the reasons is that the specific reason why the drug was prescribed usually isn’t recorded (Ahrens & Pigeot, 2007). The solution would be a controlled clinical trial, but they can be expensive and challenging to implement.
Comparison to Selection Bias
Confounding by indication is often confused as a type of selection bias, but it’s actually a type of confounding bias. Confounding isn’t actually a true “bias”, because bias is usually a result of data collection errors or measurement errors. Confounding by indication isn’t (despite the allusion) actually a bias; It’s just something that might result in confounding bias. On the other hand, selection bias is a specific type of bias that affects the types of people who are in your study; It removes the randomness you’re hoping to achieve. For example, the healthy worker effect results in healthier workers in your study, because people who are working are healthier than people who are unemployed or out of work due to a job-related disability.
Ahrens, W. & Pigeot, I. (2007). Handbook of Epidemiology. Springer Science & Business Media.
Miettinen, O. (2011). Epidemiological Research: Terms and Concepts. Springer Science & Business Media.
Stephanie Glen. "Confounding by Indication" From StatisticsHowTo.com: Elementary Statistics for the rest of us! https://www.statisticshowto.com/confounding-by-indication/
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