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Bias from unmeasured confounding is a persistent concern in observational studies, and sensitivity analysis has been proposed as a solution. In the recent years, probabilistic sensitivity analysis using either Monte Carlo sensitivity analysis (MCSA) or Bayesian sensitivity analysis (BSA) has emerged as a practical analytic strategy when there are multiple bias parameters inputs. BSA uses Bayes theorem...
Inference in gene–environment studies can sometimes exploit the assumption of Mendelian randomization that genotype and environmental exposure are independent in the population under study. Moreover, in some such problems it is reasonable to assume that the disease risk for subjects without environmental exposure will not vary with genotype. When both assumptions can be invoked, we consider the prospects...
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