Dealing with Disease Progression Data Under Nonignorable Inspection Processes: A New Perspective from Incomplete Longitudinal Data Analysis

• Activity Number: 327
• Type: Invited
• Date/Time: Tuesday, August 11, 2015 : 10:30 AM to 12:20 PM
• Sponsor: Biometrics Section
• Author(s): Grace Yi* and Baojiang Chen and Richard Cook
• Companies: University of Waterloo and University of Nebraska Medical Center and University of Waterloo
• Keywords: Model misspecification ; Model selection ; Penalized pairwise likelihood ; Longitudinal data

Abstract

Irreversible multi-state models provide a convenient framework for characterizing disease processes that arise when the states represent the degree of organ or tissue damage incurred by a progressive disease. In many settings, however, individuals are only observed at periodic clinic visits and so the precise times of the transitions are not observed. If the life history and observation processes are not independent, the observation process contains information about the life history process, and the likelihood inference based on the disease process alone is often invalid. This talk concerns the analysis of data from progressive multi-state disease processes in which individuals are scheduled to be seen at periodic pre-scheduled assessment times. We cast the problem in the framework used for incomplete longitudinal data analysis. Maximum likelihood estimation via an EM algorithm is developed for parameter estimation. Numerical studies will be presented to assess the performance of the proposed method, and data from a cohort of patients with psoriatic arthritis will be analyzed.