Frank Konietschke, Ph.D., Assistant Professor of Statistics, Department of Mathematical Sciences, University of Texas, Dallas

"Robust analyses of over-dispersed counts with varying follow-up in small samples and rare diseases"

In this talk, we consider inference methods for count data, such as the number of relapses and magnetic resonance imaging (MRI) lesion counts in multiple sclerosis (MS), or exacerbations in chronic obstructive pulmonary disease (COPD). In such clinical trials, the number of exacerbations and the follow-up time is recorded for each patient. Due to the heterogeneity of patients, the number of exacerbations cannot be assumed to follow a Poisson distribution, and over-dispersion has to be taken into account for valid statistical inferences. We derive statistical inference methods for testing null hypotheses as well as for constructing confidence intervals for the underlying treatment effects. For small sample sizes, a studentized permutation approach will be investigated. Extensive simulation studies show that the permutation based statistics tend to maintain the nominal type-1 error level or coverage probability very satisfactorily. A real data set illustrates the application of the proposed methods. The project is in cooperation with Professor Tim Friede, University of Göttingen, and Professor Markus Pauly, University of Ulm

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