Estimating Individualized Treatment Rules in Longitudinal Studies with Covariate-Driven Observation Times

TL;DR

This paper develops a new method for estimating individualized treatment rules in longitudinal studies using electronic health records, accounting for covariate-driven observation times to improve treatment decision-making.

Contribution

It introduces a covariate-dependent weighting approach within dynamic treatment regime analysis, addressing biases from non-random observation times in electronic health data.

Findings

Method yields consistent, multiply robust estimators.

Applied to UK antidepressant data to optimize BMI change.

Demonstrates improved treatment rule estimation in observational data.

Abstract

The sequential treatment decisions made by physicians to treat chronic diseases are formalized in the statistical literature as dynamic treatment regimes. To date, methods for dynamic treatment regimes have been developed under the assumption that observation times, i.e., treatment and outcome monitoring times, are determined by study investigators. That assumption is often not satisfied in electronic health records data in which the outcome, the observation times, and the treatment mechanism are associated with patients' characteristics. The treatment and observation processes can lead to spurious associations between the treatment of interest and the outcome to be optimized under the dynamic treatment regime if not adequately considered in the analysis. We address these associations by incorporating two inverse weights that are functions of a patient's covariates into dynamic weighted ordinary least squares to develop optimal single stage dynamic treatment regimes, known as individualized treatment rules. We show empirically that our methodology yields consistent, multiply robust estimators. In a cohort of new users of antidepressant drugs from the United Kingdom's Clinical Practice Research Datalink, the proposed method is used to develop an optimal treatment rule that chooses between two antidepressants to optimize a utility function related to the change in body mass index.