Variable Selection for Progressive Multistate Processes Under Intermittent Observation.

Abstract

Multistate models offer a natural framework for studying many chronic disease processes. Interest often lies in identifying which among a large list of candidate variables play a role in the progression of such processes. We consider the problem of variable selection for progressive multistate processes under intermittent observation based on penalized log-likelihood. An Expectation-Maximization (EM) algorithm is developed such that the maximization step can exploit existing software for penalized Poisson regression thereby allowing for the use of common penalty functions. Simulation studies show good performance in identifying important markers with different penalty functions. In a motivating application involving a cohort of patients with psoriatic arthritis, we identify which, among a large group of candidate HLA markers, are associated with rapid disease progression.