Extension of W-method and A-learner for multiple binary outcomes

TL;DR

This paper extends existing methods for estimating heterogeneous treatment effects to multiple binary outcomes, addressing correlation and bias issues with novel reduced-rank regression techniques.

Contribution

It introduces two new methods based on the W-method and A-learner for multiple binary outcomes, correcting bias and capturing outcome correlations.

Findings

Proposed methods effectively estimate HTE for multiple binary outcomes.

Bias in conventional A-learner estimates is identified and corrected.

Methods demonstrate improved performance in simulations and real data.

Abstract

In this study, we compared two groups, in which subjects were assigned to either the treatment or the control group. In such trials, if the efficacy of the treatment cannot be demonstrated in a population that meets the eligibility criteria, identifying the subgroups for which the treatment is effective is desirable. Such subgroups can be identified by estimating heterogeneous treatment effects (HTE). In recent years, methods for estimating HTE have increasingly relied on complex models. Although these models improve the estimation accuracy, they often sacrifice interpretability. Despite significant advancements in the methods for continuous or univariate binary outcomes, methods for multiple binary outcomes are less prevalent, and existing interpretable methods, such as the W-method and A-learner, while capable of estimating HTE for a single binary outcome, still fail to capture the correlation structure when applied to multiple binary outcomes. We thus propose two methods for estimating HTE for multiple binary outcomes: one based on the W-method and the other based on the A-learner. We also demonstrate that the conventional A-learner introduces bias in the estimation of the treatment effect. The proposed method employs a framework based on reduced-rank regression to capture the correlation structure among multiple binary outcomes. We correct for the bias inherent in the A-learner estimates and investigate the impact of this bias through numerical simulations. Finally, we demonstrate the effectiveness of the proposed method using a real data application.