title: 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 designs. The first is an extension of the semi-parametric maximum likelihood approach developed in Neuhaus, Scott and Wild (2002, Biometrika 89, 23-37) and Neuhaus, Scott and Wild (2006, Biometrics 62, 488-494) 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.
authors.affiliation: Division of Biostatistics, University of California, San Francisco, California 94143-0560, U.S.A.
authors.forename: John M
authors.initials: JM
authors.lastname: Neuhaus
authors.forename: Alastair J
authors.initials: AJ
authors.lastname: Scott
authors.forename: Christopher J
authors.initials: CJ
authors.lastname: Wild
authors.forename: Yannan
authors.initials: Y
authors.lastname: Jiang
authors.forename: Charles E
authors.initials: CE
authors.lastname: McCulloch
authors.forename: Ross
authors.initials: R
authors.lastname: Boylan
mesh_terms: D001289:Attention Deficit Disorder with Hyperactivity / Q000209:etiology; D002648:Child; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D006801:Humans; D016013:Likelihood Functions; D008137:Longitudinal Studies / Q000379:methods*; D015233:Models, Statistical; D016896:Treatment Outcome
keywords: Conditional likelihood; Retrospective sampling; Subject-specific models