Marginalized transition models and likelihood inference for longitudinal categorical data.
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.
Heagerty
Patrick J
PJ
Department of Biostatistics, University of Washington, Seattle 98195, USA. heagerty@u.washington.edu
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