Section of Biostatistics, University of Copenhagen, Copenhagen, Denmark.
Division of Biostatistics, Center for Targeted Machine, Berkeley, CA, USA.
Lifetime Data Anal. 2024 Jan;30(1):4-33. doi: 10.1007/s10985-022-09576-2. Epub 2022 Nov 7.
Targeted maximum likelihood estimation (TMLE) provides a general methodology for estimation of causal parameters in presence of high-dimensional nuisance parameters. Generally, TMLE consists of a two-step procedure that combines data-adaptive nuisance parameter estimation with semiparametric efficiency and rigorous statistical inference obtained via a targeted update step. In this paper, we demonstrate the practical applicability of TMLE based causal inference in survival and competing risks settings where event times are not confined to take place on a discrete and finite grid. We focus on estimation of causal effects of time-fixed treatment decisions on survival and absolute risk probabilities, considering different univariate and multidimensional parameters. Besides providing a general guidance to using TMLE for survival and competing risks analysis, we further describe how the previous work can be extended with the use of loss-based cross-validated estimation, also known as super learning, of the conditional hazards. We illustrate the usage of the considered methods using publicly available data from a trial on adjuvant chemotherapy for colon cancer. R software code to implement all considered algorithms and to reproduce all analyses is available in an accompanying online appendix on Github.
目标最大似然估计（TMLE）为在存在高维混杂参数的情况下估计因果参数提供了一种通用方法。通常，TMLE 由两步组成，它将数据自适应的混杂参数估计与通过有针对性的更新步骤获得的半参数效率和严格的统计推断相结合。在本文中，我们展示了基于 TMLE 的因果推断在生存和竞争风险环境中的实际应用，其中事件时间不限于离散和有限的网格上发生。我们关注的是在考虑不同的单变量和多维参数的情况下，对时间固定治疗决策对生存和绝对风险概率的因果效应的估计。除了为生存和竞争风险分析提供使用 TMLE 的一般指导外，我们还进一步描述了如何使用基于损失的交叉验证估计（也称为超级学习）扩展先前的工作，以估计条件风险。我们使用来自结肠癌辅助化疗试验的公开可用数据说明了所考虑方法的用法。可在 Github 上的随附在线附录中找到用于实现所有考虑算法和重现所有分析的 R 软件代码。
