title: The transition model test for serial dependence in mixed-effects models for binary data.
abstract: Generalized linear mixed models for longitudinal data assume that responses at different occasions are conditionally independent, given the random effects and covariates. Although this assumption is pivotal for consistent estimation, violation due to serial dependence is hard to assess by model elaboration. We therefore propose a targeted diagnostic test for serial dependence, called the transition model test (TMT), that is straightforward and computationally efficient to implement in standard software. The TMT is shown to have larger power than general misspecification tests. We also propose the targeted root mean squared error of approximation (TRSMEA) as a measure of the population misfit due to serial dependence.
authors.affiliation: 1 Department of Biostatistics, University of Copenhagen, Denmark.
authors.forename: Nina
authors.initials: N
authors.lastname: Breinegaard
authors.affiliation: 2 Graduate School of Education & Graduate Group in Biostatistics, University of California, Berkeley, USA.
authors.forename: Sophia
authors.initials: S
authors.lastname: Rabe-Hesketh
authors.affiliation: 3 Division of Epidemiology, Norwegian Institute of Public Health, Oslo, Norway.|4 CEMO, University of Oslo, Oslo, Norway.
authors.forename: Anders
authors.initials: A
authors.lastname: Skrondal
mesh_terms: D006801:Humans; D016013:Likelihood Functions; D016014:Linear Models; D008137:Longitudinal Studies; D016032:Randomized Controlled Trials as Topic / Q000379:methods; D012120:Respiration Disorders / Q000188:drug therapy; D012984:Software
keywords: Diagnostic test; dynamic model; generalized linear mixed model; longitudinal data; misspecification; panel data; specification test