JSM 2012 Home

JSM 2012 Online Program

The views expressed here are those of the individual authors and not necessarily those of the JSM sponsors, their officers, or their staff.

Online Program Home

Abstract Details

Activity Number: 471
Type: Contributed
Date/Time: Wednesday, August 1, 2012 : 8:30 AM to 10:20 AM
Sponsor: Biopharmaceutical Section
Abstract - #306584
Title: Parametric Models for MRI Count Data in Multiple Sclerosis Clinical Trials: A Robustness Study
Author(s): Mallikarjuna Rettiganti*+ and Haikady Nagaraja
Companies: University of Arkansas for Medical Sciences and The Ohio State University
Address: Biostatistics Program, Slot 512-43, Little Rock, AR, 72202, United States
Keywords: multiple sclerosis ; negative binomial ; Poisson-inverse gaussian ; likelihood ratio, score, wald ; robustness ; type I error

Magnetic resonance imaging (MRI) lesion count data in multiple sclerosis (MS) clinical trials tend to be over dispersed with respect to the Poisson distribution. Mixed-Poisson models such as negative binomial (NB) distribution and the Poisson-Inverse Gaussian (P-IG) distribution have been shown to fit well to such count data. Previous studies have shown that when distributional assumptions are satisfied, parametric tests have higher power to detect a significant treatment effect than nonparametric tests. However, robustness of these tests to violations of distributional assumptions has not been investigated before. We conducted a detailed robustness study using simulations to evaluate the performance of asymptotic and exact parametric tests such as the likelihood ratio test, Rao's score test and several Wald tests (WT) to these violations. We considered two scenarios: i) True distribution is NB; assumption is P-IG and ii) True distribution is P-IG; assumption is NB. Overall, for case i), one of the exact WTs has the highest power while maintaining below nominal Type I error rates. For case ii), the exact WT has the least Type I error among others and had reasonably high power.

The address information is for the authors that have a + after their name.
Authors who are presenting talks have a * after their name.

Back to the full JSM 2012 program

2012 JSM Online Program Home

For information, contact jsm@amstat.org or phone (888) 231-3473.

If you have questions about the Continuing Education program, please contact the Education Department.