🤖 AI Summary
When longitudinal outcomes are truncated by death, causal effects become challenging to define and estimate, and existing methods often lack clear causal assumptions and appropriate estimands. This study develops a unified framework that clarifies the definitional challenges and identification assumptions underlying various causal estimands in the presence of truncation by death. It proposes an integrated characterization combining stratum-specific average causal effects with restricted mean survival time, thereby revealing the intrinsically multifactorial nature of the problem. Building on Bayesian inference, the authors derive corresponding estimation procedures and evaluate their performance through simulations and real data from a randomized controlled trial on amyotrophic lateral sclerosis. The results demonstrate that the proposed framework yields a more comprehensive and accurate assessment of treatment effects.
📝 Abstract
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.