Simplifying and Contextualizing Sensitivity to Unmeasured Confounding Tipping Point Analyses

Abstract

The strength of evidence provided by epidemiological and observational studies is inherently limited by the potential for unmeasured confounding. Thus, we would expect every observational study to include a quantitative sensitivity to unmeasured confounding analysis. However, we reviewed 90 recent studies with statistically significant findings, published in top tier journals, and found 41 mentioned the issue of unmeasured confounding as a limitation, but only 4 included a quantitative sensitivity analysis. Moreover, the rule of thumb that considers effects 2 or greater as robust can be misleading in being too low for studies missing an important confounder and too high for studies that extensively control for confounding. We simplify the seminal work of Rosenbaum and Rubin (1983) and Lin, Pstay, and Kronmal (1998). We focus on three key quantities: the observed bound of the confi- dence interval closest to the null, a plausible residual effect size for an unmeasured binary confounder, and a realistic prevalence difference for this hypothetical confounder. We offer guidelines to researchers for anchoring the tipping point analysis in the context of the study and provide examples.

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Event
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Washington, DC
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