MARGINAL MODELS FOR LONGITUDINAL COUNT DATA WITH DROPOUTS

TitleMARGINAL MODELS FOR LONGITUDINAL COUNT DATA WITH DROPOUTS
Publication TypeJournal Article
Year of Publication2020
AuthorsZubair, S, Sinha, SK
JournalJournal of Statistical Research
Volume54
Start Page27-42
ISSN Number0256 - 422 X
KeywordsCount data, Generalized estimating equation, longitudinal study, Missing response, Weighted generalized estimating equation
Abstract

In this article, we investigate marginal models for analyzing incomplete longitudinal count data with dropouts. Specifically, we explore commonly used generalized estimating equations and weighted generalized estimating equations for fitting log-linear models to count data in the presence of monotone missing responses. A series of simulations were carried out to examine the finite-sample properties of the estimators in the presence of both correctly specified and misspecified dropout mechanisms. An application is provided using actual longitudinal survey data from the Health and Retirement Study (HRS) (HRS, 2019)

DOI10.47302/jsr.2020540102
Citation Key11062