Modeling symptom progression to identify informative subjects for a new Huntington's disease clinical trial is problematic since time to diagnosis, a key covariate, can be heavily censored. Imputation is an appealing strategy where censored covariates are replaced with their conditional means, but existing methods saw over 200% bias under heavy censoring. Calculating these conditional means well requires estimating and then integrating over the survival function of the censored covariate from the censored value to infinity. To estimate the survival function flexibly, existing methods use the semiparametric Cox model with Breslow's estimator, leaving the integrand for the conditional means (the estimated survival function) undefined beyond the observed data. The integral is then estimated up to the largest observed covariate value, and this approximation can cut off the tail of the survival function and lead to severe bias, particularly under heavy censoring. We propose a hybrid approach that splices together the semiparametric survival estimator with a parametric extension, making it possible to approximate the integral up to infinity. In simulation studies, our proposed approach of extrapolation then imputation substantially reduces the bias seen with existing imputation methods, even when the parametric extension was misspecified. We further demonstrate how imputing with corrected conditional means helps to prioritize patients for future clinical trials.

翻译：症状进展建模对识别亨廷顿病新临床试验的受试者至关重要，但关键协变量——诊断时间——可能高度删失。插补是替换删失协变量为其条件均值的有效策略，但现有方法在高度删失下偏差超过200%。准确计算条件均值需估计删失协变量的生存函数，并从删失值到无穷大积分。为灵活估计生存函数，现有方法使用带Breslow估计的半参数Cox模型，导致条件均值的被积函数（估计的生存函数）在观测数据之外无法定义。积分仅估算至最大观测协变量值，这种近似可能截断生存函数尾部，尤其在高度删失下引发严重偏差。我们提出混合方法，将半参数生存估计与参数扩展拼接，实现至无穷大的积分近似。模拟研究表明，即使参数扩展设定有误，我们的外推后插补方法仍显著降低现有方法的偏差。进一步展示修正条件均值插补如何帮助优先选择未来临床试验患者。