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：当存在治疗效应异质性时，根据患者特征推荐治疗方案可改善医疗效果。近年来，最大化预期结局的最优治疗规则估计问题备受关注。然而，针对结局存在右删失场景的研究相对较少，特别是关于估计量的实际应用。本研究通过模拟实验评估了最优治疗规则估计量及其对应预期结局估计量的有限样本表现。模拟设计基于生物医学与公共卫生研究中常见场景：观测性数据、生存时间可能存在右删失，且研究目标在于估算最大化生存概率的基线治疗方案决策。我们在一系列模拟场景下比较了多种结局回归与直接搜索估计方法在最优治疗规则估计中的表现。结果表明，灵活建模结局的方法（包括治疗规则呈非线性时）具有较优表现。本研究的R语言复现代码已发布于GitHub平台。