Abstract
Typical statistical analysis of epidemiologic data captures uncertainty due to random sampling variation, but ignores more systematic sources of variation such as selection bias, measurement error, and unobserved confounding. Such sources are often only mentioned via qualitative caveats, perhaps under the heading of ‘study limitations.’ Recently, however, there has been considerable interest and advancement in probabilistic methodologies for more integrated statistical analysis. Such techniques hold the promise of replacing a confidence interval reflecting only random sampling variation with an interval reflecting all, or at least more, sources of uncertainty. We survey and appraise the recent literature in this area, giving some prominence to the use of Bayesian statistical methodology.
Highlights
Much of the methodological literature on inferring exposure-disease relationships from observational data looks, either implicitly or explicitly, at the best-case situation: a random sample from the studyInt
We focus on what inferences we might draw in the face of concern about unobserved confounding
Probabilistic sensitivity analysis is a new suite of methods to help epidemiologists deal with bias in observational studies
Summary
Much of the methodological literature on inferring exposure-disease relationships from observational data looks, either implicitly or explicitly, at the best-case situation: a random sample from the studyInt. It arises in part because parameter estimates from statistical models explaining the conditional distribution of disease outcome given exposure are susceptible to bias as a result of unacknowledged exposure measurement error.
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