Defining a causal estimand for a longitudinal outcome truncated by death is challenging, because the outcome may be undefined at the end of follow-up. Although a range of estimands and several estimators have been proposed, guidance on the underlying causal assumptions and on the contexts in which each estimand is most appropriate remains limited. We propose a framework to clarify the challenges of defining causal estimands in a longitudinal setting with censoring due to death. Within this framework, we review existing estimands and make explicit the assumptions required for their identification and estimation. We develop Bayesian estimators for each estimand and compare their behavior in a simulation study. Finally, we illustrate the proposed approach using data from a randomized controlled trial in amyotrophic lateral sclerosis. We show that the main difficulty arises from the lack of a natural notion of ordering and distance for outcomes truncated by death. This leads to an inherently multifactorial problem. In this context, the stratified average causal effect, combined with restricted mean survival time, provides a more complete characterisation of treatment effects.

翻译：定义因死亡截断的纵向结局的因果估计量具有挑战性，因为在随访结束时结局可能无法定义。尽管已有学者提出了一系列估计量和多种估计方法，但关于其潜在因果假设以及每种估计量最适用情境的指导仍然有限。我们提出一个框架来阐明在存在死亡删失的纵向设置中定义因果估计量的挑战。基于该框架，我们回顾了现有估计量，并明确其识别与估计所需的前提假设。针对每种估计量，我们开发了贝叶斯估计方法，并通过模拟研究比较其行为特性。最后，利用肌萎缩侧索硬化症的随机对照试验数据对提出的方法进行实例验证。研究表明，主要困难源于死亡截断结局缺乏自然的排序和距离概念，这构成了一个本质上的多因素问题。在此背景下，分层平均因果效应结合受限平均生存时间可更完整地刻画治疗效果。