Biostatistics Seminar: BST 290: Michael Donohue, UC San Diego
DATE: Tuesday, November 18, 2014
LOCATION: 1147 Math Sciences Building
SPEAKER: MICHAEL DONOHUE, Division of Biostatistics, UC San Diego
Title: “Modeling long-term disease progression”
Abstract: We discuss regression approaches for estimating long-term multivariate progression (or growth) curves in the absence of long-term follow-up. We demonstrate an iterative backfitting algorithm and fully Bayesian approaches to simultaneous estimation of subject-specific (latent) “disease-time,” covariate effects, and long-term multivariate progression curves. The Bayesian approach is a multivariate extension of the smoothing and regression spline approach to measurement error problems proposed by Berry, Carroll, and Ruppert (2002). We present basic simulations and preliminary analysis of Alzheimer’s Disease Neuroimaging Initiative (ADNI) data.