Marginalized transition models and likelihood inference for longitudinal categorical data.

Patrick J Heagerty

doi:10.1111/j.0006-341x.2002.00342.x

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Marginalized transition models and likelihood inference for longitudinal categorical data.

Patrick J Heagerty

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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.

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Patrick J Heagerty. 2002. Marginalized transition models and likelihood inference for longitudinal categorical data.. https://doi.org/10.1111/j.0006-341x.2002.00342.x

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