Gao Fei, Liu Guanghan, Zeng Donglin, Diao Guoqing, Heyse Joseph F, Ibrahim Joseph G
a Department of Biostatistics , University of North Carolina , Chapel Hill , North Carolina , USA.
b Merck & Co., Inc. , Whitehouse Station , New Jersey , USA.
J Biopharm Stat. 2017;27(3):358-372. doi: 10.1080/10543406.2017.1289957. Epub 2017 Feb 7.
Missing data are common in longitudinal clinical trials. How to handle missing data is critical for both sponsors and regulatory agencies to assess treatment effect from the trials. Recently, a control-based imputation has been proposed, where the missing data are imputed based on the assumption that patients who discontinued the test drug will have a similar response profile to the patients in the control group. Under control-based imputation, the variance estimation may be biased using Rubin's formula which could produce biased statistical inferences. We evaluate several statistical methods for obtaining appropriate variances under control-based imputation for analysis of repeated binary outcomes with monotone missing data and show that both the analytical method developed by Robins & Wang and the nonparametric bootstrap method provide more appropriate variance estimates under various simulation settings. We use the methods in an application of an antidepressant Phase III clinical trial and give discussion and recommendations on method performance and preference.
在纵向临床试验中，缺失数据很常见。如何处理缺失数据对于申办者和监管机构评估试验中的治疗效果至关重要。最近，有人提出了一种基于对照的插补方法，即基于这样的假设来插补缺失数据：停用试验药物的患者与对照组患者将具有相似的反应特征。在基于对照的插补方法下，使用鲁宾公式进行方差估计可能会产生偏差，从而导致统计推断出现偏差。我们评估了几种统计方法，以便在基于对照的插补方法下获得适当的方差，用于分析具有单调缺失数据的重复二元结局，并表明罗宾斯和王开发的分析方法以及非参数自助法在各种模拟设置下都能提供更合适的方差估计。我们将这些方法应用于一项抗抑郁药物III期临床试验，并对方法性能和偏好进行了讨论和建议。
