title: Generalizability in nongaussian longitudinal clinical trial data based on generalized linear mixed models.
abstract: This work investigates how generalizability, an extension of reliability, can be defined and estimated based on longitudinal data sequences resulting from, for example, clinical studies. Useful and intuitive approximate expressions are derived based on generalized linear mixed models. Data from four double-blind, randomized clinical trials into schizophrenia motivate the research and are used to estimate generalizability for a binary response parameter.
authors.affiliation: Tibotec, Johnson & Johnson, Mechelen, Belgium. tvangene@tibbe.jnj.com
authors.forename: Tony
authors.initials: T
authors.lastname: Vangeneugden
authors.forename: Geert
authors.initials: G
authors.lastname: Molenberghs
authors.forename: Annouschka
authors.initials: A
authors.lastname: Laenen
authors.forename: Ariel
authors.initials: A
authors.lastname: Alonso
authors.forename: Helena
authors.initials: H
authors.lastname: Geys
mesh_terms: D002986:Clinical Trials as Topic / Q000706:statistics & numerical data*; D016014:Linear Models; D008137:Longitudinal Studies; D016011:Normal Distribution