SPYCE：一种用于早期亨廷顿病试验的双稳健估计器，适用于结果依赖删失情况

中文摘要 AI 辅助

神经退行性疾病的临床试验必须确定敏感终点，即能快速变化以检测治疗效果的结果。在亨廷顿病中，这需要衡量参与者接近1期时结果如何变化。然而，许多参与者在达到此阶段前退出研究，导致其到1期的时间被右删失。估计结果变化需要1期时间和研究退出时间的模型。当结果较差的参与者更早退出时，这种结果依赖删失会使现有估计器产生矛盾结果。我们引入了SPYCE，一种双稳健估计器，在任一模型正确设定时都一致，能实现最小方差，允许两个模型非参数估计且不牺牲效率。应用于PREDICT-HD的观测数据时，SPYCE解决了当前矛盾，确定尾状核和壳核体积比为最有前景的敏感终点，并表明每组仅需241名参与者就能检测治疗效果，而无法处理结果依赖删失的估计器则需数十万参与者。

英文摘要

Clinical trials for neurodegenerative diseases must identify sensitive endpoints -- outcomes that change rapidly enough to detect treatment effects. In Huntington disease, this requires measuring how outcomes change as participants approach Stage 1. Yet many participants exit studies before reaching this stage, making their time to Stage 1 right-censored. Estimating how outcomes change requires models for both time to Stage 1 and time to study exit. When participants with worse outcomes exit earlier, this outcome-dependent censoring causes existing estimators to produce contradictory results: for the same cognitive outcome, one estimator suggests improvement while another shows decline. Existing estimators either ignore outcome-dependent censoring or require one model to be correctly specified, with no protection when it is not. We introduce SPYCE, a doubly robust estimator (consistent when either model is correctly specified) that achieves the smallest possible variance and allows both models to be estimated nonparametrically without sacrificing efficiency. Applied to data from PREDICT-HD, an observational Huntington disease study, SPYCE resolves current contradictions, identifies caudate and putamen volume ratios as the most promising sensitive endpoints, and shows that as few as 241 participants per arm are needed to detect treatment effects, versus hundreds of thousands under estimators that cannot handle outcome-dependent censoring.

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81 pages, 3 figures