Lui Kung-Jong, Chang Kuang-Chao
Department of Mathematics and Statistics, College of Sciences, San Diego State University, San Diego, CA, USA
Department of Statistics and Information Science, Fu-Jen Catholic University, New Taipei, Taiwan, ROC.
Stat Methods Med Res. 2016 Aug;25(4):1272-89. doi: 10.1177/0962280213477022. Epub 2013 Mar 12.
Since therapeutic efficacy is often measured by multiple endpoints, it will be of use if one can incorporate the information on various variables of response into procedures for testing noninferiority to improve power of a univariate test procedure for each individual variable. On the basis of the proposed mixed effects logistic regression model for multivariate binary data under the matched-pairs design, we develop procedures for testing noninferiority with respect to the odds ratio in multivariate binary data under the matched-pair design. We discuss use of Bonferroni's and Scheffe's methods to control the inflation in Type I error due to multiple tests. We further employ Monte Carlo simulation to evaluate and compare the performance of these test procedures. Finally, we use the data taken from a crossover clinical trial that monitored several adverse events of an antidepressive drug to illustrate the use of test procedures derived here.
由于治疗效果通常通过多个终点指标来衡量，如果能够将关于各种反应变量的信息纳入非劣效性检验程序，以提高针对每个个体变量的单变量检验程序的功效，那将会很有用。基于所提出的匹配对设计下多变量二元数据的混合效应逻辑回归模型，我们开发了匹配对设计下多变量二元数据中关于优势比的非劣效性检验程序。我们讨论了使用邦费罗尼方法和谢费方法来控制由于多次检验导致的一类错误膨胀。我们进一步采用蒙特卡罗模拟来评估和比较这些检验程序的性能。最后，我们使用来自一项交叉临床试验的数据（该试验监测了一种抗抑郁药物的几种不良事件）来说明此处推导的检验程序的使用。
