Monte Carlo sensitivity analysis for unmeasured confounding in dynamic treatment regimes.

ABSTRACT: 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 invo

SUBMITTER: Rose EJ 

PROVIDER: S-EPMC11426919 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature