Gao Fei, Liu Guanghan F, Zeng Donglin, Xu Lei, Lin Bridget, Diao Guoqing, Golm Gregory, Heyse Joseph F, Ibrahim Joseph G
Department of Biostatistics, University of North Carolina, Chapel Hill, NC, USA.
Merck Sharp & Dohme Corp., North Wales, PA, USA.
Pharm Stat. 2017 Nov;16(6):424-432. doi: 10.1002/pst.1821. Epub 2017 Aug 22.
In clinical trials, missing data commonly arise through nonadherence to the randomized treatment or to study procedure. For trials in which recurrent event endpoints are of interests, conventional analyses using the proportional intensity model or the count model assume that the data are missing at random, which cannot be tested using the observed data alone. Thus, sensitivity analyses are recommended. We implement the control-based multiple imputation as sensitivity analyses for the recurrent event data. We model the recurrent event using a piecewise exponential proportional intensity model with frailty and sample the parameters from the posterior distribution. We impute the number of events after dropped out and correct the variance estimation using a bootstrap procedure. We apply the method to an application of sitagliptin study.
在临床试验中，缺失数据通常是由于不遵守随机治疗方案或研究程序而产生的。对于以复发事件终点为关注对象的试验，使用比例强度模型或计数模型的传统分析假定数据是随机缺失的，而这仅通过观察数据是无法检验的。因此，建议进行敏感性分析。我们实施基于对照的多重插补作为复发事件数据的敏感性分析。我们使用具有脆弱性的分段指数比例强度模型对复发事件进行建模，并从后验分布中对参数进行抽样。我们对失访后的事件数进行插补，并使用自助法程序校正方差估计。我们将该方法应用于西他列汀研究的一个实例。
