Goodness of Fit of a Joint Model for Event Time and Nonignorable Missing Longitudinal QOL Data

• Activity Number: 351
• Type: Topic Contributed
• Date/Time: Wednesday, August 14, 2002 : 2:00 PM to 3:50 PM
• Sponsor: IMS
• Author(s): Sneh Gulati*+ and Jean-François Dupuy and Mounir Mesbah
• Affiliation(s): Florida International University and University of South Brittany and Université de Bretagne-Sud
• Keywords: goodness-of-fit, Cox Model, longitudinal data

Abstract

In many survival studies, one is interested not only in the duration time to some terminal event, but also in repeated measurements made on a time-dependent covariate. In these studies, subjects often drop out of the study before the occurrence of the event of interest, and the problem of interest then becomes modeling the relationship between the time to dropout and the internal covariate. Jean-Francois Dupuy and Mounir Mesbah (2001) proposed a model that described this relationship when the value of the covariate at the dropout time is unobserved. This model combined a first-order Markov model for the longitudinally measured covariate with a time-dependent Cox model for the dropout process. In this paper, we propose a test statistic to test the validity of the above-mentioned model. Using the techniques developed by Lin (1991), we develop a class of estimators of the regression parameters using weight functions. The test statistic is then a function of the standard maximum likelihood estimators and the estimators based on the weight function. The asymptotic distribution of the statistic is studied, and the procedure is also applied to some sets of real data.