Statistics Seminar: STA 290
Thursday, February 28th, 2013 at 4:10pm, MSB 1147 (Colloquium Room)
Refreshments at 3:30pm in MSB 4110 (Statistics Lounge)
Speaker: John Neuhaus, Division of Biostatistics, University of California, San Francisco
Title: "Likelihood-based analysis of longitudinal data from outcome-dependent sampling designs"
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 a series of papers by Neuhaus, Scott and Wild 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.