  Data-driven methods for personalizing treatment assignment have garnered much
attention from clinicians and researchers. Dynamic treatment regimes formalize
this through a sequence of decision rules that map individual patient
characteristics to a recommended treatment. Observational studies are commonly
used for estimating dynamic treatment regimes due to the potentially
prohibitive costs of conducting sequential multiple assignment randomized
trials. However, estimating a dynamic treatment regime from observational data
can lead to bias in the estimated regime due to unmeasured confounding.
Sensitivity analyses are useful for assessing how robust the conclusions of the
study are to a potential unmeasured confounder. A Monte Carlo sensitivity
analysis is a probabilistic approach that involves positing and sampling from
distributions for the parameters governing the bias. We propose a method for
performing a Monte Carlo sensitivity analysis of the bias due to unmeasured
confounding in the estimation of dynamic treatment regimes. We demonstrate the
performance of the proposed procedure with a simulation study and apply it to
an observational study examining tailoring the use of antidepressants for
reducing symptoms of depression using data from Kaiser Permanente Washington
(KPWA).
