Likelihood-Based Analysis of Longitudinal Data from Outcome-Dependent Sampling Designs

• Activity Number: 394
• Type: Invited
• Date/Time: Tuesday, August 6, 2013 : 2:00 PM to 3:50 PM
• Sponsor: Section on Statistics in Epidemiology
• Author(s): John Neuhaus*+ and Alastair Scott and Chris J. Wild and Yannan Jiang and Charles McCulloch
• Companies: University of California, San Francisco and University of Auckland and University of Auckland and University of Auckland and University of California, San Francisco
• Keywords: Conditional likelihood ; Retrospective sampling ; Subject-specific models

Abstract

Investigators commonly gather longitudinal data to assess changes in responses over time and to relate these changes to within-subject changes in predictors. With rare or expensive outcomes such as uncommon diseases and costly radiologic measurements, outcome-dependent sampling plans can improve estimation efficiency and reduce cost. Longitudinal follow up of subjects gathered in an initial outcome-dependent sample can then be used to study the trajectories of responses over time and to assess the association of changes in predictors within subjects with change in response. In this talk we develop two likelihood-based approaches for fitting generalized linear mixed models (GLMMs) to longitudinal data from a wide variety of outcome-dependent sampling designs. The first is an extension of the semi-parametric maximum likelihood approach developed in papers by Scott, Wild and Neuhaus and applies quite generally. The second approach is an adaptation of standard conditional likelihood methods and is limited to random intercept models with a canonical link. Data from a study of Attention Deficit Hyperactivity Disorder in children motivates the work and illustrates the findings.