This manuscript implements a maximum likelihood based approach that is appropriate for equally spaced longitudinal count data with over-dispersion, so that the variance of the outcome variable is larger than expected for the assumed Poisson distribution. We implement the proposed method in the analysis of two data sets and make comparisons with the semi-parametric generalized estimating equations (GEE) approach that incorrectly ignores the over-dispersion. The simulations demonstrate that the proposed method has better small sample efficiency than GEE. We also provide code in R that can be used to recreate the analysis results that we provide in this manuscript.
Gamerman, Victoria; Guerra, Matthew; and Shults, Justine, "Maximum Likelihood Based Analysis of Equally Spaced Longitudinal Count Data with Specified Marginal Means, First-order Antedependence, and Linear Conditional Expectations" (March 2016). UPenn Biostatistics Working Papers. Working Paper 45.