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Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and Cox...
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Updated: May 25, 2026

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
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Power analyses for negative binomial models with application to multiple sclerosis clinical trials.

Mallik Rettiganti1, H N Nagaraja

  • 1Department of Statistics, The Ohio State University, Columbus, Ohio 43210-1247, USA.

Journal of Biopharmaceutical Statistics
|January 19, 2012
PubMed
Summary

Negative binomial models offer significant sample size reductions for clinical trials in relapsing-remitting multiple sclerosis (RRMS). Likelihood ratio tests in parallel group (PG) and baseline versus treatment (BVT) trials require fewer patients compared to nonparametric methods.

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Area of Science:

  • Biostatistics
  • Clinical Trials
  • Neuroscience

Background:

  • Relapsing-remitting multiple sclerosis (RRMS) clinical trials often involve analyzing magnetic resonance imaging (MRI)-based brain lesion counts.
  • Accurate statistical methods are crucial for efficient trial design and sample size estimation.

Purpose of the Study:

  • To evaluate the utility of negative binomial (NB) models and associated statistical tests for analyzing MRI-based brain lesion count data in RRMS trials.
  • To compare the power and sample size requirements of NB-based tests against nonparametric methods.

Main Methods:

  • Application of negative binomial (NB) models to MRI-derived brain lesion counts from parallel group (PG) and baseline versus treatment (BVT) trial designs.
  • Description and application of likelihood ratio (LR), score, and Wald tests within the NB framework.
  • Power analyses and sample size estimations using simulated percentiles of exact test statistic distributions.

Main Results:

  • The likelihood ratio (LR) test demonstrated substantial reductions in sample size requirements compared to nonparametric tests.
  • Sample size reductions ranged from 30-45% for PG trials and 25-60% for BVT trials.
  • Negative binomial models provide a statistically robust framework for lesion count data analysis.

Conclusions:

  • Negative binomial models, particularly the LR test, offer significant advantages in sample size efficiency for RRMS clinical trials.
  • These findings support the adoption of NB models for optimizing the design and resource allocation in neuroimaging studies for MS.
  • The proposed methods enhance statistical power and reduce the number of participants needed for detecting treatment effects.