title: A modeling approach to the analysis of nerve regenerative experiments.
abstract: Many experiments aiming at the investigation of nerve repair involve elaborate testing over a certain time period. Data arising from such experiments are often analyzed by time-point. Such a cross-sectional approach is often very inefficient. In this paper, we consider a case study in which repeated measurements of two response variables assumed to be Poisson distributed are obtained. We show how a repeated measures modeling approach, based on generalized linear models, can handle both responses in one model and improve the inference in the nerve repair experiments. The benefits of the model as well as problems that can occur are illustrated and discussed.
authors.affiliation: Center for Statistics, Hasselt University, Center for Statistics, Diepenbeek, Belgium. suzy.vansanden@uhasselt.be
authors.forename: Suzy
authors.initials: S
authors.lastname: Van Sanden
authors.forename: Luc
authors.initials: L
authors.lastname: Wouters
authors.forename: Tomasz
authors.initials: T
authors.lastname: Burzykowski
authors.forename: Geert
authors.initials: G
authors.lastname: Molenberghs
authors.forename: Marie
authors.initials: M
authors.lastname: Van Remoortere
authors.forename: Theo
authors.initials: T
authors.lastname: Meert
authors.forename: Luc
authors.initials: L
authors.lastname: Bijnens
mesh_terms: D000465:Algorithms; D000704:Analysis of Variance; D003430:Cross-Sectional Studies; D006801:Humans; D016014:Linear Models; D008137:Longitudinal Studies; D015233:Models, Statistical; D015999:Multivariate Analysis; D009416:Nerve Regeneration / Q000502:physiology*; D016012:Poisson Distribution; D012107:Research Design; D012984:Software