Likelihood approach for count data in longitudinal experiments

In many cancer studies and clinical research, repeated observations of response variables are taken over time on each individual in one or more treatment groups. In such cases the repeated observations of each vector response are likely to be correlated and the autocorrelation structure for the repeated data plays a significant role in the estimation of regression parameters. A random intercept model for count data is developed using exact maximum-likelihood estimation via numerical integration. A simulation study is performed to compare the proposed methodology with the traditional generalized linear mixed model (GLMM) approach and with the GLMM when penalized quasi-likelihood method is used to perform maximum-likelihood estimation. The methodology is illustrated by analyzing data sets containing longitudinal measures of number of tumors in an experiment of carcinogenesis to study the influence of lipids in the development of cancer. © 2007 Elsevier B.V. All rights reserved.