title: Maximum likelihood based analysis of equally spaced longitudinal count data with first-order antedependence and overdispersion.
abstract: 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 seizure data and a subset of German Socio-Economic Panel data. To demonstrate the importance of correctly modeling the over-dispersion, we make comparisons with the semi-parametric generalized estimating equations approach that incorrectly ignores any over-dispersion in the data. Our simulations demonstrate that accounting for over-dispersion results in improved small-sample efficiency and appropriate coverage probabilities. We also provide code in R so that readers can implement our approach in their own analyses.
authors.affiliation: Boehringer-Ingelheim Pharmaceuticals, Inc., 900 Ridgebury Road, Ridgefield, CT 06877 USA.
authors.forename: Victoria
authors.initials: V
authors.lastname: Gamerman
authors.affiliation: Division of Biometrics III, OB, OTS, CDER, FDA, Silver Spring, MD 20993 USA.
authors.forename: Matthew
authors.initials: M
authors.lastname: Guerra
authors.affiliation: Department of Biostatistics, University of Pennsylvania, 423 Guardian Drive, Philadelphia, PA 19104 USA.
authors.forename: Justine
authors.identifier: 0000-0002-0266-0980
authors.initials: J
authors.lastname: Shults
keywords: Count data; First-order antedependence; Generalized estimating equations; Markov property; Maximum likelihood estimation; Over-dispersion