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arXiv · 2410.07483

Doubly robust estimation and sensitivity analysis with outcomes truncated by death in multi-arm clinical trials

Jiaqi Tong·Chao Cheng·Guangyu Tong·Michael O. Harhay·Fan Li
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Abstract

In clinical trials, the observation of participant outcomes may frequently be hindered by death, leading to ambiguity in defining a scientifically meaningful final outcome for those who die. Principal stratification methods are valuable tools for addressing the average causal effect among always-survivors, i.e., the average treatment effect among a subpopulation defined as those who would survive regardless of treatment assignment. Although robust methods for the truncation-by-death problem in two-arm clinical trials have been previously studied, its expansion to multi-arm clinical trials remains elusive. In this article, we study the identification of a class of survivor average causal effect estimands with multiple treatments under monotonicity and principal ignorability, and first propose simple weighting and regression approaches for point estimation. As a further improvement, we derive the efficient influence function to motivate doubly robust estimators for the survivor average causal effects in multi-arm clinical trials. We also propose sensitivity methods under violations of key causal assumptions. Extensive simulations are conducted to investigate the finite-sample performance of the proposed methods against the existing methods, and a real data example is used to illustrate how to operationalize the proposed estimators and the sensitivity methods in practice.

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Jiaqi Tong, Chao Cheng, Guangyu Tong, Michael O. Harhay, Fan Li. 2025-08-29. Doubly robust estimation and sensitivity analysis with outcomes truncated by death in multi-arm clinical trials. https://arxiv.org/abs/2410.07483

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