We develop a novel doubly-robust (DR) imputation framework for longitudinal studies with monotone dropout, motivated by the informative dropout that is common in FDA-regulated trials for Alzheimer's disease. In this approach, the missing data are first imputed using a doubly-robust augmented inverse probability weighting (AIPW) estimator, then the imputed completed data are substituted into a full-data estimating equation, and the estimate is obtained using standard software. The imputed completed data may be inspected and compared to the observed data, and standard model diagnostics are available. The same imputed completed data can be used for several different estimands, such as subgroup analyses in a clinical trial, allowing for reduced computation and increased consistency across analyses. We present two specific DR imputation estimators, AIPW-I and AIPW-S, study their theoretical properties, and investigate their performance by simulation. AIPW-S has substantially reduced computational burden compared to many other DR estimators, at the cost of some loss of efficiency and the requirement of stronger assumptions. Simulation studies support the theoretical properties and good performance of the DR imputation framework. Importantly, we demonstrate their ability to address time-varying covariates, such as a time by treatment interaction. We illustrate using data from a large randomized Phase III trial investigating the effect of donepezil in Alzheimer's disease, from the Alzheimer's Disease Cooperative Study (ADCS) group.

翻译：我们针对具有单调缺失模式的纵向研究，提出了一种新颖的双重稳健（DR）插补框架，其动机源于美国食品药品监督管理局（FDA）监管的阿尔茨海默病试验中常见的非随机缺失机制。在该方法中，首先利用双重稳健的增强逆概率加权（AIPW）估计量对缺失数据进行插补，然后将插补后的完整数据代入完整数据估计方程，并通过标准软件获得估计值。插补后的完整数据可进行检查并与观测数据进行比较，同时可进行标准模型诊断。同一套插补后的完整数据可用于多种不同的估计目标，例如临床试验中的亚组分析，从而减少计算量并提高分析间的一致性。我们提出了两种具体的DR插补估计量——AIPW-I和AIPW-S，研究了它们的理论性质，并通过模拟实验评估了它们的性能。与许多其他DR估计量相比，AIPW-S的计算负担大幅降低，但代价是效率略有损失且需依赖更强的假设。模拟研究支持DR插补框架的理论性质及其良好性能。重要的是，我们展示了该方法处理时变协变量（如时间与治疗的交互作用）的能力。我们利用来自阿尔茨海默病合作研究（ADCS）小组的一项大型随机III期试验数据（探究多奈哌齐对阿尔茨海默病的影响）进行了实例验证。