# Measurement error and precision medicine: error-prone tailoring covariates in dynamic treatment regimes

## TL;DR

This paper examines how measurement error in covariates affects the development of dynamic treatment regimes in precision medicine, demonstrating that correction techniques improve treatment decision accuracy.

## Contribution

It introduces measurement error correction methods specifically for dynamic treatment regimes, addressing a gap in precision medicine research.

## Key findings

- Measurement error correction improves treatment regime accuracy
- Simulation and theoretical results support correction methods
- Application to STAR*D study illustrates practical benefits

## 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.