A new method for clustered survival data]{A new method for clustered survival data: Estimation of treatment effect heterogeneity and variable selection
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集群生存数据的新方法：处理效应异质性估计与变量选择
- 研究问题
- 如何在集群、删失且可能缺失的生存数据中探索处理效应异质性、识别相关子群，并进行灵活模型下的变量选择？
- 方法与思路
- 作者从 riAFT-BART 后验样本分析个体生存处理效应，以变量纳入比例构造置换选择方法，并结合自助插补处理缺失。
- 材料陈述的贡献
- 摘要将既有方法扩展至异质性探索与变量选择，通过模拟和重症新冠案例展示用途，并称方法已在相应 R 包中提供。
- 阅读关注点
- 应核对因果识别与删失处理条件、探索性子群分析的误差控制，以及置换选择和插补策略如何应对集群依赖。
- 阅读边界
- 所据为 arXiv 预印本，同行评审或期刊接收未核实；摘要未说明混杂、删失和缺失假设，也未给出具体模拟结果。
原文摘要
We recently developed a new method riAFT-BART to draw causal inferences about population treatment effect on patient survival from clustered and censored survival data while accounting for the multilevel data structure. The practical utility of this method goes beyond the estimation of population average treatment effect. In this work, we exposit how riAFT-BART can be used to solve two important statistical questions with clustered survival data: estimating the treatment effect heterogeneity and variable selection. Leveraging the likelihood-based machine learning, we describe a way in which we can draw posterior samples of the individual survival treatment effect from riAFT-BART model runs, and use the drawn posterior samples to perform an exploratory treatment effect heterogeneity analysis to identify subpopulations who may experience differential treatment effects than population average effects. There is sparse literature on methods for variable selection among clustered and censored survival data, particularly ones using flexible modeling techniques. We propose a permutation based approach using the predictor's variable inclusion proportion supplied by the riAFT-BART model for variable selection. To address the missing data issue frequently encountered in health databases, we propose a strategy to combine bootstrap imputation and riAFT-BART for variable selection among incomplete clustered survival data. We conduct an expansive simulation study to examine the practical operating characteristics of our proposed methods. Finally, we demonstrate the methods via a case study of predictors for in-hospital mortality among severe COVID-19 patients and estimating the heterogeneous treatment effects of three COVID-specific medications. The methods developed in this work are readily available in the $\R$ package $\textsf{riAFTBART}$.