Trajectory-Based Individualized Treatment Rules

TL;DR

This paper introduces a novel longitudinal trajectory-based method for estimating individualized treatment rules that leverages outcome trajectories over time, improving treatment personalization especially in mental health studies with missing data.

Contribution

It develops a new method that incorporates longitudinal outcome trajectories and baseline variables into ITR estimation, addressing missing data issues and enhancing treatment decision accuracy.

Findings

The new method outperforms traditional approaches in simulations.

Application to depression trial demonstrates improved treatment rules.

Trajectory-based ITR reduces impact of missing data.

Abstract

A core component of precision medicine research involves optimizing individualized treatment rules (ITRs) based on patient characteristics. Many studies used to estimate ITRs are longitudinal in nature, collecting outcomes over time. Yet, to date, methods developed to estimate ITRs often ignore the longitudinal structure of the data. Information available from the longitudinal nature of the data can be especially useful in mental health studies. Although treatment means might appear similar, understanding the trajectory of outcomes over time can reveal important differences between treatments and placebo effects. This longitudinal perspective is especially beneficial in mental health research, where subtle shifts in outcome patterns can hold significant implications. Despite numerous studies involving the collection of outcome data across various time points, most precision medicine methods used to develop ITRs overlook the information available from the longitudinal structure. The prevalence of missing data in such studies exacerbates the issue, as neglecting the longitudinal nature of the data can significantly impair the effectiveness of treatment rules. This paper develops a powerful longitudinal trajectory-based ITR construction method that incorporates baseline variables, via a single-index or biosignature, into the modeling of longitudinal outcomes. This trajectory-based ITR approach substantially minimizes the negative impact of missing data compared to more traditional ITR approaches. The approach is illustrated through simulation studies and a clinical trial for depression, contrasting it with more traditional ITRs that ignore longitudinal information.