SPYCE: A Doubly Robust Estimator for Trials Targeting Early Huntington Disease under Outcome-Dependent Censoring

2026-07-20

AI Summary

In clinical trials for Huntington’s disease, outcome-dependent censoring has led to conflicting estimates of early biomarker trajectories, impeding the identification of sensitive endpoints and efficient sample size planning. This work proposes SPYCE, a novel method that achieves doubly robust estimation in this setting by jointly modeling time to progression to Stage 1 and time to dropout. The approach permits nonparametric specification of both models without sacrificing efficiency and attains the semiparametric efficiency bound. Empirical analysis using PREDICT-HD data demonstrates that SPYCE resolves existing estimation discrepancies and identifies the caudate-to-putamen volume ratio as the most sensitive endpoint, reducing the required sample size per group from hundreds of thousands to just 241 participants.

Abstract

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.

Problem

• outcome-dependent censoring
• Huntington disease
• sensitive endpoints
• right-censoring
• clinical trials

Innovation

• doubly robust estimator
• outcome-dependent censoring
• nonparametric estimation
• efficient inference
• sensitive endpoints