Successive classification learning for estimating quantile optimal treatment regimes

TL;DR

This paper introduces a new method for estimating quantile optimal treatment regimes by reformulating the problem as a successive classification task, improving efficiency and handling complex and discrete outcomes effectively.

Contribution

It proposes a novel successive classification approach for quantile OTRs and introduces a smoothing technique for discrete outcomes, with theoretical guarantees and empirical validation.

Findings

Method outperforms existing approaches in simulations

Effective handling of discrete outcomes with smoothing technique

Theoretical guarantees ensure consistency and effectiveness

Abstract

Quantile optimal treatment regimes (OTRs) aim to assign treatments that maximize a specified quantile of patients' outcomes. Compared to treatment regimes that target the mean outcomes, quantile OTRs offer fairer regimes when a lower quantile is selected, as it improves outcomes for vulnerable patients. In this paper, we propose a novel method for estimating quantile OTRs by reformulating the problem as a successive classification task, solvable via training a sequence of classifiers, each successive classifier built on the output of its predecessors. This reformulation enables us to leverage the powerful machine learning technique to enhance computational efficiency and handle complex decision boundaries. We also investigate the estimation of quantile OTRs when outcomes are discrete, a setting that has received limited attention in the literature. A key challenge is that direct extensions of existing methods to discrete outcomes often lead to inconsistency and ineffectiveness issues. To overcome this, we introduce a smoothing technique that maps discrete outcomes to continuous surrogates, enabling consistent and effective estimation. We provide theoretical guarantees to support our methodology, and demonstrate its superior performance through comprehensive simulation studies and real-data analysis.