Journal article

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

Marco Alfo, Murray Aitkin

STATISTICS AND COMPUTING | SPRINGER | Published : 2006

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..

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University of Melbourne Researchers