A Characterization of Missingness-at-Random in a Generalized Shared-Parameter Joint Modeling Framework for Longitudinal and Time-to-Event Data and Sensitivity Analysis

• Activity Number: 523
• Type: Topic Contributed
• Date/Time: Wednesday, August 12, 2015 : 10:30 AM to 12:20 PM
• Sponsor: Mental Health Statistics Section
• Author(s): Geert Molenberghs* and Edmund Njeru Njagi and Michael G. Kenward and Geert Verbeke and Dimitris Rizopoulos
• Companies: Universiteit Hasselt/Katholieke Universiteit Leuven and Universiteit Hasselt and London School of Hygiene and Tropical Medicine and KU Leuven/Universiteit Hasselt and Erasmus Medical Center
• Keywords: joint modeling ; longitudinal data ; missing data ; sensitivity analysis ; coarsening

Abstract

Rubin (1976) classified missing data as MCAR, MAR, and MNAR. Given that models for missing data often make unverifiable assumptions about the missing value mechanism, sensitivity analysis is needed. In joint modeling of time-to-event and longitudinal data, similar issues arise. The longitudinal covariate may be measured with error, its values are likewise only available at the specific time points at that the patient appears at the clinic for longitudinal measurements, and the time-to-event may also be censored.

Undeniably, there is a strong connection between the missing data and the joint longitudinal and time-to-event settings, the theme of this work. We build an extended shared random effects joint model, similar in spirit to that of Creemers et al (2011). An added layer of complexity is that data can be coarsened in various ways: the longitudinal sequence can be incomplete; the time-to-event outcome can be censored; both of these can occur simultaneously. Within the extended framework, we provide a characterization of MAR, consistent to the one in the missing data setting, and juxtapose it with more conventional joint models. This opens routes for sensitivity analysis.