Modelling and sample size reestimation for longitudinal count data with incomplete follow up. (January 2019)

• Record Type: Journal Article
• Authors: Asendorf, Thomas; Henderson, Robin; Schmidli, Heinz; Friede, Tim
• Is Part Of: Statistical methods in medical research. Volume 28:Number 1(2019)
• Page Start: 117
• Page End: 133
• Publication Date: 2019-01
• Subjects: Adaptive design -- lesion counts -- sample size reestimation -- negative binomial -- discrete autoregressive process -- time dependent; Medicine -- Research -- Statistical methods -- Periodicals; Research -- Periodicals; Review Literature -- Periodicals; Statistics -- methods -- Periodicals; Médecine -- Recherche -- Méthodes statistiques -- Périodiques; 610.727
• DOI: 10.1177/0962280217715664
• Languages: English
• ISSNs: 0962-2802

Abstract

We consider modelling and inference as well as sample size estimation and reestimation for clinical trials with longitudinal count data as outcomes. Our approach is general but is rooted in design and analysis of multiple sclerosis trials where lesion counts obtained by magnetic resonance imaging are important endpoints. We adopt a binomial thinning model that allows for correlated counts with marginal Poisson or negative binomial distributions. Methods for sample size planning and blinded sample size reestimation for randomised controlled clinical trials with such outcomes are developed. The models and approaches are applicable to data with incomplete observations. A simulation study is conducted to assess the effectiveness of sample size estimation and blinded sample size reestimation methods. Sample sizes attained through these procedures are shown to maintain the desired study power without inflating the type I error. Data from a recent trial in patients with secondary progressive multiple sclerosis illustrate the modelling approach.