Two-Sample Test for Correlated Data Under Missing Not at Random

• Activity Number: 187
• Type: Contributed
• Date/Time: Monday, August 10, 2015 : 10:30 AM to 12:20 PM
• Sponsor: Biometrics Section
• Author(s): Yi Cai* and Yong Chen
• Companies: The University of Texas at Houston and The University of Texas School of Public Health
• Keywords: Composite likelihood ; Longitudinal data ; Outcome dependent sampling ; Two-sample test

Abstract

Traditional methods for two-sample test such as t-test and Wilcoxon rank sum test lead to incorrect Type I errors when applied to clustered or longitudinal data.Recent work which extends two-sample test for clustered data typically requires certain assumptions on correlation structure and/or cluster size being noninformative. In this paper, we propose a score test based on a novel construction of pseudolikelihood for correlated data where assumption on correlation structure is not required and missing not at random is allowed. In addition, the proposed test can capture the differences in higher order moments such as variances and skewness between two groups. Projection theory is used to derive the covariance matrix which can be empirically estimated. The proposed test is shown to have a simple chi-squared distribution. Simulation studies are conducted to evaluate the proposed test comparing to the existing methods. An analysis of self-reported weight loss data is presented to illustrate the proposed test.