Diao Guoqing, Liu Guanghan F, Zeng Donglin, Zhang Yilong, Golm Gregory, Heyse Joseph F, Ibrahim Joseph G
Department of Biostatistics and Bioinformatics, The George Washington University, Washington, District of Columbia, U.S.A.
Merck & Co., Inc., North Wales, Pennsylvania, U.S.A.
Stat Biopharm Res. 2022;14(2):153-161. doi: 10.1080/19466315.2020.1819403. Epub 2020 Nov 5.
Missing data are commonly encountered in clinical trials due to dropout or nonadherence to study procedures. In trials in which recurrent events are of interest, the observed count can be an undercount of the events if a patient drops out before the end of the study. In many applications, the data are not necessarily missing at random and it is often not possible to test the missing at random assumption. Consequently, it is critical to conduct sensitivity analysis. We develop a control-based multiple imputation method for recurrent events data, where patients who drop out of the study are assumed to have a similar response profile to those in the control group after dropping out. Specifically, we consider the copy reference approach and the jump to reference approach. We model the recurrent event data using a semiparametric proportional intensity frailty model with the baseline hazard function completely unspecified. We develop nonparametric maximum likelihood estimation and inference procedures. We then impute the missing data based on the large sample distribution of the resulting estimators. The variance estimation is corrected by a bootstrap procedure. Simulation studies demonstrate the proposed method performs well in practical settings. We provide applications to two clinical trials.
由于患者退出或不遵守研究程序，临床试验中经常会遇到数据缺失的情况。在关注复发事件的试验中，如果患者在研究结束前退出，观察到的事件计数可能会低于实际事件数。在许多应用中，数据不一定是随机缺失的，而且往往无法检验随机缺失假设。因此，进行敏感性分析至关重要。我们针对复发事件数据开发了一种基于对照的多重填补方法，其中假设退出研究的患者在退出后具有与对照组患者相似的反应特征。具体来说，我们考虑复制参考方法和跳转到参考方法。我们使用半参数比例强度脆弱模型对复发事件数据进行建模，其中基线风险函数完全未指定。我们开发了非参数最大似然估计和推断程序。然后，我们根据所得估计量的大样本分布对缺失数据进行填补。通过自助法对方差估计进行校正。模拟研究表明，所提出的方法在实际应用中表现良好。我们提供了两个临床试验的应用实例。
