Measurement error and precision medicine: error-prone tailoring covariates in dynamic treatment regimes
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.
Keywords
Cite
@article{arxiv.1907.11659,
title = {Measurement error and precision medicine: error-prone tailoring covariates in dynamic treatment regimes},
author = {Dylan Spicker and Michael Wallace},
journal= {arXiv preprint arXiv:1907.11659},
year = {2020}
}