There is a fast-growing literature on estimating optimal treatment rules directly by maximizing the expected outcome. In biomedical studies and operations applications, censored survival outcome is frequently observed, in which case the truncated mean survival time and survival probability are of great interest. In this paper, we propose two robust criteria for learning optimal treatment rules with censored survival outcomes; the former one targets an optimal treatment rule maximizing the truncated mean survival time, where the cutoff is specified by a given quantile such as median; the latter one targets an optimal treatment rule maximizing buffered survival probabilities, where the predetermined threshold is adjusted to account for the truncated mean survival time. We develop a sampling-based difference-of-convex algorithm for learning the proposed optimal treatment rules, and provide theoretical justifications for them. In simulation studies, our estimators show improved performance compared to existing methods. We also demonstrate the proposed method using AIDS clinical trial data.

翻译：近年来，通过直接最大化期望结果来估计最优治疗规则的研究文献快速增长。在生物医学研究和运营应用中，删失生存结局经常被观测到，此时截尾平均生存时间和生存概率备受关注。本文针对具有删失生存结局的最优治疗规则学习提出了两个稳健准则：前者以最大化截尾平均生存时间为目标，其截断点由给定分位数（如中位数）指定；后者以最大化缓冲生存概率为目标，其中预设阈值经过调整以考虑截尾平均生存时间。我们为学习所提出的最优治疗规则开发了一种基于采样的凸差算法，并提供了相应的理论依据。在模拟研究中，我们的估计器相较于现有方法表现出更优的性能。我们还通过艾滋病临床试验数据展示了所提出方法的应用。