ABSTRACT
Precision medicine incorporates patient-level covariates to tailor treatment decisions, seeking to improve outcomes. In longitudinal studies with time-varying covariates and sequential treatment decisions, precision medicine can be formalized with dynamic treatment regimes (DTRs): sequences of covariate-dependent treatment rules. To date, the precision medicine literature has not addressed a ubiquitous concern in health research-measurement error-where observed data deviate from the truth. We discuss the consequences of ignoring measurement error in the context of DTRs, focusing on challenges unique to precision medicine. We show-through simulation and theoretical results-that relatively simple measurement error correction techniques can lead to substantial improvements over uncorrected analyses, and apply these findings to the sequenced treatment alternatives to relieve depression study.
