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Variance component models for longitudinal count data with baseline information: Epilepsy data revisited

Lookup NU author(s): Emeritus Professor Murray Aitkin

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Abstract

Random effect models have often been used in longitudinal data analysis since they allow for association among repeated measurements due to unobserved heterogeneity. Various approaches have been proposed to extend mixed models for repeated count data to include dependence on baseline counts. Dependence between baseline counts and individual-specific random effects result in a complex form of the (conditional) likelihood. An approximate solution can be achieved ignoring this dependence, but this approach could result in biased parameter estimates and in wrong inferences. We propose a computationally feasible approach to overcome this problem, leaving the random effect distribution unspecified. In this context, we show how the EM algorithm for nonparametric maximum likelihood (NPML) can be extended to deal with dependence of repeated measures on baseline counts. © Springer Science + Business Media, LLC 2006.

Publication metadata

Author(s): Alfo M, Aitkin M

Publication type: Article

Publication status: Published

Journal: Statistics and Computing

Year: 2006

Volume: 16

Issue: 3

Pages: 231-238

ISSN (print): 0960-3174

ISSN (electronic): 1573-1375

Publisher: Springer Netherlands

URL: http://dx.doi.org/10.1007/s11222-006-7072-5

DOI: 10.1007/s11222-006-7072-5

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