Inferences for the Poisson-Inverse Gaussian Distribution with Application to Multiple Sclerosis Clinical Trials

• Activity Number: 177
• Type: Contributed
• Date/Time: Monday, August 1, 2011 : 10:30 AM to 12:20 PM
• Sponsor: Biopharmaceutical Section
• Author(s): Mallikarjuna Rettiganti*+ and Haikady Nagaraja
• Companies: University of Arkansas for Medical Sciences and The Ohio State University
• Keywords: Poisson Inverse Gaussian ; likelihood ratio ; score ; Wald ; multiple sclerosis

Abstract

Magnetic resonance imaging (MRI) based new brain lesion counts are widely used to monitor disease progression in relapsing remitting multiple sclerosis (RRMS) clinical trials. These data generally tend to be over dispersed with respect to a Poisson distribution. It has been shown that the Poisson-Inverse Gaussian (P-IG) distribution fits better than the negative binomial to MRI data in RRMS patients selected for lesion activity during the baseline scan. In this paper we use the P-IG distribution to model MRI lesion count data from RRMS parallel group trials. We propose asymptotic and simulation based exact parametric tests for the treatment effect such as the likelihood ratio (LR), score and Wald tests. The exact tests maintain precise Type I error levels for small sample sizes when the asymptotic tests fail to do so. The LR test remained empirically unbiased and resulted in a 30-50% reduction in sample sizes required when compared to the Wilcoxon rank sum (WRS) test. One of the Wald tests had the highest power to detect a reduction in the number of lesion counts and provided a 40-57% reduction in sample sizes when compared to the WRS test.