Multiple Imputation Methods for Multivariate One-Sided Tests with Missing Data

• Activity Number: 669
• Type: Topic Contributed
• Date/Time: Thursday, August 8, 2013 : 10:30 AM to 12:20 PM
• Sponsor: Biometrics Section
• Author(s): Tao Wang*+ and Lang Wu
• Companies: Eli Lilly and Company and The University of British Columbia
• Keywords: Multiple imputation ; Constrained inference ; Multivariate hypothesis testing ; Order-restricted inference ; Likelihood ratio test ; Wald-type test

Abstract

Multivariate one-sided hypotheses testing problems arise frequently in practice. Various tests haven been developed. In practice, there are often missing values in multivariate data. In this case, standard testing procedures based on complete data may not be applicable or may perform poorly if the missing data are discarded. In this article, we propose several multiple imputation methods for multivariate one-sided testing problem with missing data. The proposed methods are evaluated using simulations. A real data example is presented to illustrate the methods.