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 (STAR*D) study.
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