Sequential Advantage Selection for Optimal Treatment Regimes

TL;DR

This paper introduces a sequential advantage selection method based on a modified S-score to identify variables with qualitative interactions for optimal treatment regimes, improving decision-making in clinical studies.

Contribution

It develops a new sequential variable selection approach that accounts for joint effects and qualitative interactions, enhancing the reliability of treatment regime estimation.

Findings

Method performs well in simulations

Handles large covariate sets with small samples

Applied successfully to depression trial data

Abstract

Variable selection for optimal treatment regime in a clinical trial or an observational study is getting more attention. Most existing variable selection techniques focused on selecting variables that are important for prediction, therefore some variables that are poor in prediction but are critical for decision-making may be ignored. A qualitative interaction of a variable with treatment arises when treatment effect changes direction as the value of this variable varies. The qualitative interaction indicates the importance of this variable for decision-making. Gunter et al. (2011) proposed S-score which characterizes the magnitude of qualitative interaction of each variable with treatment individually. In this article, we developed a sequential advantage selection method based on the modified S-score. Our method selects qualitatively interacted variables sequentially, and hence excludes marginally important but jointly unimportant variables {or vice versa}. The optimal treatment regime based on variables selected via joint model is more comprehensive and reliable. With the proposed stopping criteria, our method can handle a large amount of covariates even if sample size is small. Simulation results show our method performs well in practical settings. We further applied our method to data from a clinical trial for depression.