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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/20855

Title: Generating correlated and/or overdispersed count data: A SAS implementation
Authors: Kalema, George
Molenberghs, Geert
Issue Date: 2015
Citation: Journal of statistical software, 70 (C1), pag. 1-20
Abstract: Analysis of longitudinal count data has, for long, been done using a generalized linear mixed model (GLMM), in its Poisson-normal version, to account for correlation by specifying normal random effects. Univariate counts are often handled with the negativebinomial (NEGBIN) model taking into account overdispersion by use of Gamma random effects. Inherently though, longitudinal count data commonly exhibit both features of correlation and overdispersion simultaneously, necessitating analysis methodology that can account for both. The introduction of the combined model (CM) by (Molenberghs, Verbeke, and Dem´etrio 2007) and (Molenberghs, Verbeke, Dem´etrio, and Vieira 2010) serves this purpose, not only for count data but for the general exponential family of distributions. Here, a Poisson model is specified as the parent distribution of the data with a normally distributed random effect at the subject or cluster level and/or a gamma distribution at observation level. The GLMM and NEGBIN are special cases. Data can be simulated from (1) the general CM, with random effects, or, (2) its marginal version directly. This paper discusses an implementation of (1) in SAS software (SAS Institute Inc. (2011)). One needs to reflect on the mean of both the combined (hierarchical) and marginal models in order to generate correlated and/or overdispersed counts. A pre-specification of the desired marginal mean (in terms of covariates and marginal parameters), a marginal variance-covariance structure and the hierarchical mean (in terms of covariates and regression parameters) is required. The implied hierarchical parameters, the variance-covariance matrix of the random effects, and the variance-covariance matrix of the overdispersion part are then derived from which correlated Poisson data are generated. Sample calls of the SAS macro are presented as well as output.
Notes: Kalema, G (reprint author), Makerere Univ, Sch Stat & Appl Econ, POB 7062, Kampala, Uganda. george.kalema@uhasselt.be; geert.molenberghs@uhasselt.be
URI: http://hdl.handle.net/1942/20855
DOI: 10.18637/jss.v070.c01
ISI #: 000373920300001
ISSN: 1548-7660
Category: A1
Type: Journal Contribution
Validation: ecoom, 2017
Appears in Collections: Research publications

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