title: Statistical models for autocorrelated count data.
abstract: A generalized linear mixed model is an increasingly popular choice for the modelling of correlated, non-normal responses in a regression setting. A number of methods are currently available for fitting a generalized linear mixed model including Monte-Carlo Markov-Chain maximum likelihood algorithms, approximate maximum likelihood (PQL), iterative bias correction, and others. Of interest in this paper is to compare the parameter estimation of the various methods in the modelling of a count data set, the incidence of polio in the USA over the period 1970-1983, using a longlinear generalized linear mixed model with an autoregressive correlation structure. Despite the fact that all of these methods are considered valid modelling techniques, we find that parameter estimates and standard errors differ substantially between analyses, particularly in the estimation of the parameters describing the random effects distribution. A small simulation study is helpful in understanding some of these differences. The methods lead to reasonably similar predictions for future observations, with small differences observed in some monthly counts.
authors.affiliation: Department of Statistics, University of South Carolina, Columbia, SC 29208, USA. kerrie@stat.sc.edu
authors.forename: Kerrie P
authors.initials: KP
authors.lastname: Nelson
authors.forename: Brian G
authors.initials: BG
authors.lastname: Leroux
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D001699:Biometry / Q000379:methods; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D004812:Epidemiologic Methods; D006801:Humans; D015994:Incidence; D016013:Likelihood Functions; D016014:Linear Models; D008390:Markov Chains; D009010:Monte Carlo Method; D011051:Poliomyelitis / Q000453:epidemiology; D012984:Software; D013269:Stochastic Processes; D014481:United States / Q000453:epidemiology