个体随机试验与群组随机试验中存活期间估计量的双重稳健估计
Doubly robust estimation of while-alive estimands in individually-randomized and cluster-randomized trials
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中文总结 AI 辅助
本研究针对个体与群组随机试验，提出存活期间估计量的双重稳健估计方法，验证其性质并通过模拟与再分析佐证方法有效性。
中文摘要 AI 辅助
慢性病场景下的随机试验常通过受死亡截断的复发性非致命事件衡量治疗获益，传统汇总方法要么丢弃复发事件、将治疗效应与生存相混淆，要么将死亡视为删失并丧失因果解释。存活期间估计量衡量每单位存活时间的事件负担，但针对暴露加权存活期间率的双重稳健估计尚未发展，尤其在群组随机试验（CRTs）中。我们基于局部Nelson-Aalen表示法构建双重稳健估计量，采用增广估计方程以针对终端事件的边际风险与存活者中的加权复发事件率；该估计量可通过预先指定的临床权重处理多种事件类型，且若删失模型或结果工作模型之一设定正确则保持一致性。针对群组随机试验，我们在信息群组规模下定义了一对新的个体平均与群组平均估计量，基于群组水平影响函数进行推断。我们建立了分量级双重稳健性与渐近正态性，通过模拟验证理论，并对两项已完成随机试验的数据进行再分析以说明所提方法。
英文摘要
Randomized trials in chronic disease settings often measure treatment benefit through recurrent non-fatal events that are truncated by death, where conventional summaries either discard recurrences, conflate the treatment effect with survival, or treat death as censoring and forfeit a causal interpretation. While-alive estimands measure event burden per unit time alive, but doubly robust estimation for the exposure-weighted while-alive rate remains undeveloped, particularly in cluster-randomized trials (CRTs). We develop a doubly robust estimator based on a local Nelson-Aalen representation, with augmented estimating equations targeting the marginal hazard of the terminal event and the weighted recurrent event rate among those alive; the estimator accommodates multiple event types through prespecified clinical weights and remains consistent if either the censoring model or the outcome working models are correctly specified. For CRTs, we define a new pair of individual-average and cluster-average estimands under informative cluster size, with inference based on cluster-level influence functions. We establish component-wise double robustness and asymptotic normality, corroborate the theory in simulations, and illustrate the methods with reanalyses of data from two completed randomized trials.
