When you perform a scientific study, the issue of confounding is very important. In general, the aim of your study is to assess the true effect that an exposure (e.g. a risk factor or an intervention) has on the outcome of interest (e.g. a disease or a clinical outcome). Confounding prevents you from measuring this true effect. If your study has confounding, the conclusion of your research may be invalid. A factor that causes confounding in your study is called a confounder. Below you see a schematic overview of how a confounder may effect your study.

2017_counfounding_small

A confounder can be almost anything you can measure. However, there are three requirements for a given factor to be a confounder. These are:

  1. A confounder has to be associated with the level of exposure (e.g. different proportion in intervention compared to control group).
  2. A confounder has to be associated with the outcome of interest (e.g. different proportion in present compared to absent outcome).
  3. A confounder cannot be on the causal pathway between the exposure and the outcome of interest (e.g. renal failure is on the causal pathway between high blood pressure and heart failure).

Solution?

If you want to make sure your study results are valid, you need to limit the effects of confounding as much as possible. This can be done by study design: restriction, randomization, or matching. Alternatively, you can adjust for known confounding in the statistical analysis: stratification or coefficient adjustment. It is important to think about confounding both in the study protocol and when you perform analysis of your results.

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