Likelihood-based analysis of longitudinal data from outcome-related 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, and more generally outcome-related, sampling plans can improve estimation efficiency and reduce cost. Longitudinal follow up of subjects gathered in an initial outcome-related 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 article, we develop two likelihood-based approaches for fitting generalized linear mixed models (GLMMs) to longitudinal data from a wide variety of outcome-related sampling des

SUBMITTER: Neuhaus JM 

PROVIDER: S-EPMC3954410 | biostudies-literature | 2014 Mar

REPOSITORIES: biostudies-literature