Comparative effectiveness research frequently addresses a time-to-event outcome and can require unique considerations in the presence of treatment noncompliance. Motivated by the challenges in addressing noncompliance in the ADAPTABLE pragmatic trial, we develop a multiply robust estimator to estimate the principal survival causal effects under the principal ignorability and monotonicity assumption. The multiply robust estimator involves several working models including that for the treatment assignment, the compliance strata, censoring, and time-to-event of interest. The proposed estimator is consistent even if one, and sometimes two, of the working models are misspecified. We apply the multiply robust method in the ADAPTABLE trial to evaluate the effect of low- versus high-dose aspirin assignment on patients' death and hospitalization from cardiovascular diseases. We find that, comparing to low-dose assignment, assignment to the high-dose leads to differential effects among always high-dose takers, compliers, and always low-dose takers. Such treatment effect heterogeneity contributes to the null intention-to-treatment effect, and suggests that policy makers should design personalized strategies based on potential compliance patterns to maximize treatment benefits to the entire study population. We further perform a formal sensitivity analysis for investigating the robustness of our causal conclusions under violation of two identification assumptions specific to noncompliance.

翻译：比较有效性研究常涉及时间至事件结局，而在存在治疗非依从性时需考虑独特问题。受ADAPTABLE实用性试验中处理非依从性挑战的启发，我们开发了一种多重稳健估计量，以在主要可忽略性和单调性假设下估计主要生存因果效应。该多重稳健估计量涉及多个工作模型，包括治疗分配模型、依从分层模型、删失模型及感兴趣的时间至事件模型。即使其中一至两个工作模型被错误设定，该估计量仍保持一致性。我们将该多重稳健方法应用于ADAPTABLE试验，评估低剂量与高剂量阿司匹林分配对患者心血管疾病死亡和住院的影响。研究发现，与低剂量分配相比，高剂量分配在高剂量始终服用者、依从者及低剂量始终服用者中产生了差异化效应。这种治疗效应异质性导致了意向治疗分析的空效应，提示政策制定者应根据潜在依从模式设计个性化策略，以最大化整个研究人群的治疗获益。我们进一步开展了形式化的敏感性分析，以检验在违反非依从性两个关键识别假设时因果结论的稳健性。