title: Conditional estimation for generalized linear models when covariates are subject-specific parameters in a mixed model for longitudinal measurements.
abstract: The relationship between a primary endpoint and features of longitudinal profiles of a continuous response is often of interest, and a relevant framework is that of a generalized linear model with covariates that are subject-specific random effects in a linear mixed model for the longitudinal measurements. Naive implementation by imputing subject-specific effects from individual regression fits yields biased inference, and several methods for reducing this bias have been proposed. These require a parametric (normality) assumption on the random effects, which may be unrealistic. Adapting a strategy of Stefanski and Carroll (1987, Biometrika74, 703-716), we propose estimators for the generalized linear model parameters that require no assumptions on the random effects and yield consistent inference regardless of the true distribution. The methods are illustrated via simulation and by application to a study of bone mineral density in women transitioning to menopause.
authors.affiliation: Department of Statistics, North Carolina State University, Raleigh, North Carolina 27695-8203, USA.
authors.forename: Erning
authors.initials: E
authors.lastname: Li
authors.forename: Daowen
authors.initials: D
authors.lastname: Zhang
authors.forename: Marie
authors.initials: M
authors.lastname: Davidian
mesh_terms: D001699:Biometry; D015519:Bone Density; D001851:Bone Diseases, Metabolic / Q000378:metabolism / Q000652:urine; D005260:Female; D006801:Humans; D016014:Linear Models; D016015:Logistic Models; D008137:Longitudinal Studies; D008593:Menopause / Q000378:metabolism / Q000652:urine; D008875:Middle Aged; D008954:Models, Biological; D011374:Progesterone / Q000652:urine
chemical_list: D011374:Progesterone