Estimating treatment effects in trials with outcome data truncated by death: A case study on aligning estimators with estimands

作者：Tra My Pham, Brennan C Kahan, Andre Lopes, Memuna Rashid, Peter Hoskin, Ian R. White · 发表于：Clinical Trials · 年份：2025 · DOI：10.1177/17407745251360645 · 被引用次数：4 · 研究领域：Advanced Causal Inference Techniques、Statistical Methods and Bayesian Inference、Statistical Methods and Inference

BACKGROUND/AIMS: Randomised clinical trials assessing treatment effects on health outcomes (e.g. quality of life) can be affected by data truncation by death, where some patients die before their outcome measure is assessed and their data become undefined after death. The ICH E9(R1) addendum on estimands discusses four strategies for handling such terminal intercurrent events: hypothetical, composite, while-alive, and principal stratum. While the addendum emphasises the importance of aligning statistical methods of analysis (i.e. estimators) with estimands, it does not provide specific guidance and consideration on the choice of estimators in practice. We aim to (1) demonstrate how some statistical methods commonly used in trials can be used to estimate different intercurrent event strategies for handling data truncation by death; and (2) describe how missing outcome data (e.g. due to missed assessments or loss to follow-up) can be handled for each estimator. METHOD: We use data from SCORAD, a non-inferiority randomised trial comparing single-fraction versus multifraction radiotherapy on ambulatory status at 8 weeks (primary outcome) among patients with spinal canal compression from metastatic cancer. Here, we estimate the effect of radiotherapy on quality of life (secondary outcome), quantified by the difference in mean global health status between the two groups at 8 weeks. We outline the strategies for handling death and describe a selection of commonly used estimators cor...