Investigators have several design, measurement, and analytic tools to detect and reduce bias in epidemiological studies. One such approach, “negative controls,” has been used on an ad hoc basis for decades. A formal approach has recently been suggested for its use to detect confounding, selection, and measurement bias in epidemiological studies.1,2 Negative controls in epidemiological studies are analogous to negative controls in laboratory experiments, in which investigators test for problems with the experimental method by leaving out an essential ingredient, inactivating the hypothesized active ingredient, or checking for an effect that would be impossible by the hypothesized mechanism.1 A placebo treatment group in a randomized trial is an example of a negative control exposure (leaving out an essential ingredient) that helps remove bias that can result from participant or practitioner knowledge of an individual’s treatment assignment—the placebo treatment is susceptible to the same bias structure as the actual treatment but is causally unrelated to the outcome of interest.
Arnold BF, Ercumen A. Negative Control Outcomes: A Tool to Detect Bias in Randomized Trials. JAMA. 2016;316(24):2597–2598. doi:10.1001/jama.2016.17700
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