In recent years, there has been growing interest in causal machine learning estimators for quantifying subject-specific effects of a binary treatment on time-to-event outcomes. Estimation approaches have been proposed which attenuate the inherent regularisation bias in machine learning predictions, with each of these estimators addressing measured confounding, right censoring, and in some cases, left truncation. However, the existing approaches are found to exhibit suboptimal finite-sample performance, with none of the existing estimators fully leveraging the temporal structure of the data, yielding non-smooth treatment effects over time. We address these limitations by introducing surv-iTMLE, a targeted learning procedure for estimating the difference in the conditional survival probabilities under two treatments. Unlike existing estimators, surv-iTMLE accommodates both left truncation and right censoring while enforcing smoothness and boundedness of the estimated treatment effect curve over time. Through extensive simulation studies under both right censoring and left truncation scenarios, we demonstrate that surv-iTMLE outperforms existing methods in terms of bias and smoothness of time-varying effect estimates in finite samples. We then illustrate surv-iTMLE's practical utility by exploring heterogeneity in the effects of immunotherapy on survival among non-small cell lung cancer (NSCLC) patients, revealing clinically meaningful temporal patterns that existing estimators may obscure.

翻译：近年来，因果机器学习估计器在量化二元处理对时间-事件结局的个体特异性效应方面引起了广泛关注。已有多种估计方法被提出以减轻机器学习预测中固有的正则化偏差，这些估计器均能处理测量混杂、右删失，部分情况下还能处理左截断问题。然而，现有方法在有限样本下的表现欠佳，且均未能充分利用数据的时序结构，导致处理效应随时间非平滑变化。为解决上述局限性，我们提出surv-iTMLE——一种靶向学习流程，用于估计两种处理条件下条件生存概率的差异。与现有估计器不同，surv-iTMLE能同时处理左截断和右删失，同时强制估计的处理效应曲线随时间保持平滑且有界。通过在右删失和左截断场景下的广泛模拟研究，我们证明surv-iTMLE在有限样本下的时变效应估计偏差和平滑性方面均优于现有方法。随后，通过探索免疫治疗对非小细胞肺癌患者生存影响的异质性，我们展示了surv-iTMLE的实用价值，揭示了现有估计器可能掩盖的具有临床意义的时序模式。