Extension of W-method and A-learner for multiple binary outcomes
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AI 根据所提供的论文材料归类：摘要明确研究治疗与对照比较中的异质性治疗效应及受益亚组识别。
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面向多个二元结局的 W-method 与 A-learner 扩展
- 研究问题
- 当试验总体疗效不明显时，如何利用多个相关二元结局估计异质性治疗效应，并识别可能受益的亚组？
- 方法与思路
- 作者扩展 W-method 与 A-learner，以降秩回归刻画多个二元结局的相关结构，并修正常规 A-learner 的效应估计偏差。
- 材料陈述的贡献
- 摘要称方法同时处理多结局相关性与 A-learner 偏差，并通过数值模拟考察偏差影响、用真实数据展示方法效果。
- 阅读关注点
- 应核对治疗效应的定义、降秩结构与秩选择方式，以及偏差来源、修正条件和亚组解释如何对应多个结局。
- 阅读边界
- 这是 arXiv 预印本，同行评审及期刊接收未核实；摘要未说明治疗分配机制、识别条件及模拟和应用的具体结果。
原文摘要
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.