title: Marginalized transition models and likelihood inference for longitudinal categorical data.
abstract: Marginal generalized linear models are now frequently used for the analysis of longitudinal data. Semiparametric inference for marginal models was introduced by Liang and Zeger (1986, Biometrics 73, 13-22). This article develops a general parametric class of serial dependence models that permits likelihood-based marginal regression analysis of binary response data. The methods naturally extend the first-order Markov models of Azzalini (1994, Biometrika 81, 767-775) and prove computationally feasible for long series.
authors.affiliation: Department of Biostatistics, University of Washington, Seattle 98195, USA. heagerty@u.washington.edu
authors.forename: Patrick J
authors.initials: PJ
authors.lastname: Heagerty
mesh_terms: D001699:Biometry; D003627:Data Interpretation, Statistical; D006801:Humans; D016013:Likelihood Functions; D016014:Linear Models; D008137:Longitudinal Studies; D008390:Markov Chains; D015233:Models, Statistical; D012044:Regression Analysis; D012559:Schizophrenia / Q000209:etiology