鞅R学习器：估计时间-事件结局的时变异质性处理效应
Martingale R-learner: Estimating Time-varying Heterogeneous Treatment Effects for Time-to-event Outcomes
AI总结：
该研究针对时间-事件结局，提出鞅R学习器，通过扩展奈曼正交函数评分框架降低干扰模型偏差，用于估计时变异质性处理效应，经验验证后成功应用于饮酒对痴呆影响的分析。
AI中文摘要：
生物研究与临床证据表明，治疗反应可能随合并症、遗传变异、环境或社会经济因素等特征存在显著差异。未来精准医学需要准确评估异质性处理效应（HTE），以指导个体层面的最佳临床决策。我们提出一种函数评分框架，将传统生存数据的估计方程扩展至非参数HTTE，并相应推广了奈曼正交性，从而填补了方法学与理论上的空白。在奈曼正交函数评分框架下，我们基于条件鞅残差的分解（分解为风险集倾向得分的残差与边际鞅）开发了鞅R学习器，以此降低包括（1）边际生存和（2）风险集倾向得分在内的干扰模型对HTE估计偏差的影响。这一方法可利用机器学习的进展，为干扰函数引入灵活估计量，并达到具备先知性质的标准最优非参数估计速率。数值实验验证了与理论一致的经验性能，我们还将鞅R学习器应用于使用檀香山-亚洲老龄化研究数据估计饮酒对痴呆的影响。
英文摘要：
Biological research and clinical evidence suggest that treatment response may vary substantially along characteristics, such as comorbidities, genetic variants, environmental, or socio-economic factors. Future precision medicine requires accurate assessment of heterogeneous treatment effects (HTE) to guide optimal clinical decisions at the individual level. We introduce a functional score framework that extends the traditional estimating equations for survival data to nonparametric HTE and generalize the Neyman orthogonality accordingly, thus filling a methodological as well as theoretical gap. Under the Neyman orthogonal functional score framework, we developed the martingale R-learner based on a decomposition of the conditional martingale residuals into residuals of the risk-set propensity score and the marginal martingale, thereby reducing the impact of estimation bias in HTE from nuisance models including (1) marginal survival, and (2) risk-set propensity scores. This enables leveraging advances in machine learning and incorporates flexible estimators for the nuisance functions and attaining the standard optimal nonparametric estimation rate with the oracle property. Numerical experiments demonstrated empirical performance consistent with the theory. We applied the martingale R-learner to estimate the effect of alcohol on dementia using the Honolulu-Asia Aging Study data.
