To select outcomes for clinical trials testing experimental therapies for Huntington disease, a fatal neurodegenerative disorder, analysts model how potential outcomes change over time. Yet, subjects with Huntington disease are often observed at different levels of disease progression. To account for these differences, analysts include time to clinical diagnosis as a covariate when modeling potential outcomes, but this covariate is often censored. One popular solution is imputation, whereby we impute censored values using predictions from a model of the censored covariate given other data, then analyze the imputed dataset. However, when this imputation model is misspecified, our outcome model estimates can be biased. To address this problem, we developed a novel method, dubbed "ACE imputation." First, we model imputed values as error-prone versions of the true covariate values. Then, we correct for these errors using semiparametric theory. Specifically, we derive an outcome model estimator that is consistent, even when the censored covariate is imputed using a misspecified imputation model. Simulation results show that ACE imputation remains empirically unbiased even if the imputation model is misspecified, unlike multiple imputation which yields >100% bias. Applying our method to a Huntington disease study pinpoints outcomes for clinical trials aimed at slowing disease progression.

翻译：为选择亨廷顿病（一种致命神经退行性疾病）实验性疗法的临床试验结局指标，分析人员需构建潜在结局随时间变化的模型。然而，亨廷顿病患者常在不同疾病进展阶段被观测。为解释这些差异，分析人员在建模潜在结局时，将临床诊断时间作为协变量纳入，但该协变量常被删失。一种常见解决方案是插值法——基于其他数据对删失协变量构建预测模型，据此对删失值进行插值，再分析插值后的数据集。然而，当该插值模型设定错误时，结局模型估计会产生偏倚。为解决此问题，我们提出新型方法"ACE插值法"。首先，我们将插值值建模为真实协变量值含有测量误差的版本，随后利用半参数理论校正这些误差。具体而言，我们推导出一个在删失协变量使用错误设定插值模型时仍保持一致的结局模型估计量。模拟结果表明：即使插值模型设定错误，ACE插值法仍保持经验无偏性，而多重插值法的偏倚超过100%。将该方法应用于亨廷顿病研究，可锁定旨在延缓疾病进展的临床试验的结局指标。