Abstract
Observational research from large population databases may be affected by unmeasured confounding and time-related biases, such as immortal time bias. Modern causal inference practice applies propensity score-based methods, new-user designs, and other strategies to mitigate bias. The degree to which these methodologic approaches adequately address bias for any particular study may be difficult to measure. Recently, the incorporation of positive and negative controls has been identified as a means to assess for the impacts of residual confounding and/or time-related biases. The objective of this commentary is to describe the role of positive and negative controls in observational research. We offer recommendations for incorporating controls into critical appraisal and observational research projects.
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