Patient care may be improved by recommending treatments based on patient characteristics when there is treatment effect heterogeneity. Recently, there has been a great deal of attention focused on the estimation of optimal treatment rules that maximize expected outcomes. However, there has been comparatively less attention given to settings where the outcome is right-censored, especially with regard to the practical use of estimators. In this study, simulations were undertaken to assess the finite-sample performance of estimators for optimal treatment rules and estimators for the expected outcome under treatment rules. The simulations were motivated by the common setting in biomedical and public health research where the data is observational, survival times may be right-censored, and there is interest in estimating baseline treatment decisions to maximize survival probability. A variety of outcome regression and direct search estimation methods were compared for optimal treatment rule estimation across a range of simulation scenarios. Methods that flexibly model the outcome performed comparatively well, including in settings where the treatment rule was non-linear. R code to reproduce this study's results are available on Github.

翻译：患者护理可通过基于患者特征推荐治疗方案（在存在治疗效应异质性时）得到改善。近年来，最大化预期结局的最优治疗规则估计问题受到广泛关注，但针对结局存在右删失情形（尤其是估计量的实际应用）的研究相对不足。本研究通过模拟实验评估了最优治疗规则估计量及其在治疗规则下预期结局估计量的有限样本表现。模拟场景基于生物医学与公共卫生研究中的常见设置：观测性数据、生存时间可能存在右删失，以及需估计以最大化生存概率的基线治疗决策。我们比较了多种结局回归与直接搜索估计方法在各类模拟场景下进行最优治疗规则估计的表现。能灵活建模结局的方法（包括治疗规则非线性场景）表现出较优性能。重现本研究结果的R代码已托管于Github。