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AI 根据所提供的论文材料归类:摘要研究死亡截断下随机试验的因果估计目标及不同估计方法的表现。

反馈分类问题

Outcomes truncated by death in RCTs: a simulation study on the survivor average causal effect

随机对照试验中死亡截断结局:幸存者平均因果效应的模拟研究

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代码、数据与补充材料

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关注这篇论文的作者
Chiara Vanetta
Sven Wellmann
Leonhard Held

随机对照试验中死亡截断结局:幸存者平均因果效应的模拟研究

研究问题
死亡使连续结局无法定义或观测时,幸存者平均因果效应估计、完整案例分析和多重插补在不同目标下表现如何?
方法与思路
作者设置九种结局效应与生存效应组合,模拟每位患者的两个潜在结果,并按两种真实效应比较偏差、均方误差和覆盖率。
材料陈述的贡献
摘要称所考察情景中,幸存者效应估计与多重插补结果相近,相较完整案例分析偏差更小,并对遗漏一个协变量较稳健。
阅读关注点
应核对幸存者主层与假想估计目标的区别、不可检验假设,以及九种情景和协变量遗漏设计是否支持应用中的判断。
阅读边界
所据为 arXiv 预印本,同行评审或期刊接收未核实;模拟中的相似结果不代表估计目标相同,也不能保证一般假设违背下稳健。

原文摘要

Continuous outcome measurements truncated by death present a challenge for the estimation of unbiased treatment effects in randomized controlled trials (RCTs). One way to deal with such situations is to estimate the survivor average causal effect (SACE), but this requires making non-testable assumptions. Motivated by an ongoing RCT in very preterm infants with intraventricular hemorrhage, we performed a simulation study to compare a SACE estimator with complete case analysis (CCA) and an analysis after multiple imputation of missing outcomes. We set up 9 scenarios combining positive, negative and no treatment effect on the outcome (cognitive development) and on survival at 2 years of age. Treatment effect estimates from all methods were compared in terms of bias, mean squared error and coverage with regard to two true treatment effects: the treatment effect on the outcome used in the simulation and the SACE, which was derived by simulation of both potential outcomes per patient. Despite targeting different estimands (principal stratum estimand, hypothetical estimand), the SACE-estimator and multiple imputation gave similar estimates of the treatment effect and efficiently reduced the bias compared to CCA. Also, both methods were relatively robust to omission of one covariate in the analysis, and thus violation of relevant assumptions. Although the SACE is not without controversy, we find it useful if mortality is inherent to the study population. Some degree of violation of the required assumptions is almost certain, but may be acceptable in practice.

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