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Data-driven methods for personalizing treatment assignment have garnered muchattention from clinicians and researchers. Dynamic treatment regimes formalizethis through a sequence of decision rules that map individual patientcharacteristics to a recommended treatment. Observational studies are commonlyused for estimating dynamic treatment regimes due to the potentiallyprohibitive costs of conducting sequential multiple assignment randomizedtrials. However, estimating a dynamic treatment regime from observational datacan lead to bias in the estimated regime due to unmeasured confounding.Sensitivity analyses are useful for assessing how robust the conclusions of thestudy are to a potential unmeasured confounder. A Monte Carlo sensitivityanalysis is a probabilistic approach that involves positing and sampling fromdistributions for the parameters governing the bias. We propose a method forperforming a Monte Carlo sensitivity analysis of the bias due to unmeasuredconfounding in the estimation of dynamic treatment regimes. We demonstrate theperformance of the proposed procedure with a simulation study and apply it toan observational study examining tailoring the use of antidepressants forreducing symptoms of depression using data from Kaiser Permanente Washington(KPWA).

Monte Carlo Sensitivity Analysis for Unmeasured Confounding in Dynamic Treatment Regimes