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2016 | OriginalPaper | Chapter

14. Adaptive Poisson Regression Modeling of Multivariate Count Outcomes

Authors : George J. Knafl, Kai Ding

Published in: Adaptive Regression for Modeling Nonlinear Relationships

Publisher: Springer International Publishing

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Abstract

This chapter formulates and demonstrates adaptive fractional polynomial modeling of means and dispersions for repeatedly measured count outcomes, possibly converted to rates using offsets. Marginal modeling extends from the multivariate normal outcome context to the multivariate count/rate outcome context. However, due to the complexity in general of computing likelihoods and quasi-likelihoods (as needed to account for non-unit dispersions) for general multivariate marginal modeling, generalized estimating equations (GEE) techniques are often used instead, thereby avoiding computation of likelihoods and quasi-likelihoods. This complicates the extension of adaptive modeling to the GEE context since it is based on cross-validation (CV) scores computed from likelihoods or likelihood-like functions, but a readily computed extended likelihood is formulated for use in adaptive GEE-based modeling of multivariate count/rate outcomes. Conditional modeling also extends to the multivariate count/rate outcome context, both transition modeling and general conditional modeling. In contrast to marginal GEE-based modeling, conditional modeling of means for multivariate count/rate outcomes with unit dispersions is based on pseudolikelihoods that can be used to compute pseudolikelihood CV (PLCV) scores on which to base adaptive transition and general conditional modeling of multivariate count/rate outcomes. These marginal and conditional models can be extended to model dispersions as well as means. Example analyses of these kinds are presented of the post-baseline seizure rates per day over time for patients with epilepsy in terms of the baseline seizure rate, clinic visit, and treatment group (prescribed the drug progabide versus a placebo).

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Metadata
Title
Adaptive Poisson Regression Modeling of Multivariate Count Outcomes
Authors
George J. Knafl
Kai Ding
Copyright Year
2016
DOI
https://doi.org/10.1007/978-3-319-33946-7_14

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