title: Notes on testing noninferiority in multivariate binary data under the matched-pair design.
abstract: 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.
authors.affiliation: Department of Mathematics and Statistics, College of Sciences, San Diego State University, San Diego, CA, USA kjl@rohan.sdsu.edu.
authors.forename: Kung-Jong
authors.initials: KJ
authors.lastname: Lui
authors.affiliation: Department of Statistics and Information Science, Fu-Jen Catholic University, New Taipei, Taiwan, ROC.
authors.forename: Kuang-Chao
authors.initials: KC
authors.lastname: Chang
mesh_terms: D000928:Antidepressive Agents / Q000627:therapeutic use; D018592:Cross-Over Studies; D003627:Data Interpretation, Statistical; D000074099:Equivalence Trials as Topic; D006801:Humans; D016015:Logistic Models; D009010:Monte Carlo Method; D016017:Odds Ratio; D012107:Research Design
chemical_list: D000928:Antidepressive Agents
keywords: Odds ratio; bootstrapping; exact test; matched-pairs; multivariate binary data; noninferiority