Department of Statistics, North Carolina State University, Raleigh, NC, USA.
Merck & Co., Inc., Kenilworth, NJ, USA.
Stat Methods Med Res. 2023 Jan;32(1):181-194. doi: 10.1177/09622802221135251. Epub 2022 Nov 6.
Missing data is inevitable in longitudinal clinical trials. Conventionally, the missing at random assumption is assumed to handle missingness, which however is unverifiable empirically. Thus, sensitivity analyses are critically important to assess the robustness of the study conclusions against untestable assumptions. Toward this end, regulatory agencies and the pharmaceutical industry use sensitivity models such as return-to-baseline, control-based, and washout imputation, following the ICH E9(R1) guidance. Multiple imputation is popular in sensitivity analyses; however, it may be inefficient and result in an unsatisfying interval estimation by Rubin's combining rule. We propose distributional imputation in sensitivity analysis, which imputes each missing value by samples from its target imputation model given the observed data. Drawn on the idea of Monte Carlo integration, the distributional imputation estimator solves the mean estimating equations of the imputed dataset. It is fully efficient with theoretical guarantees. Moreover, we propose weighted bootstrap to obtain a consistent variance estimator, taking into account the variabilities due to model parameter estimation and target parameter estimation. The superiority of the distributional imputation framework is validated in the simulation study and an antidepressant longitudinal clinical trial.
在纵向临床试验中，数据缺失是不可避免的。传统上，假设随机缺失来处理缺失值，但这在经验上是无法验证的。因此，敏感性分析对于评估研究结论对不可检验假设的稳健性至关重要。为此，监管机构和制药行业按照 ICH E9(R1)指南使用敏感性模型，如回归到基线、基于对照和冲洗填补。多重填补在敏感性分析中很流行；然而，它可能效率低下，并通过 Rubin 的组合规则导致令人不满意的区间估计。我们在敏感性分析中提出分布填补，通过给定观测数据，从目标填补模型中为每个缺失值抽样进行填补。受蒙特卡罗积分思想的启发，分布填补估计量通过填补数据集的均值估计方程来求解。它具有理论保证的完全效率。此外，我们提出加权自举法来获得一致的方差估计量，同时考虑到由于模型参数估计和目标参数估计而产生的可变性。在模拟研究和抗抑郁纵向临床试验中验证了分布填补框架的优越性。
