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Updated: Jun 20, 2026
10:46
A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
Published on: December 9, 2015
Statistical methods for the analysis of relapse data in MS clinical trials
Y C Wang1, L Meyerson, Y Q Tang
1Department of Biometrics, Biogen Idec, 14 Cambridge Center, Cambridge, MA 02142, USA. yong-cheng.wang@biogenidec.com
Journal of the Neurological Sciences
|August 28, 2009
Summary
For multiple sclerosis (MS) clinical trials, negative binomial regression is recommended for assessing treatment efficacy in reducing relapse frequency due to its simplicity and efficiency in handling complex patient data.
Area of Science:
- Neurology
- Biostatistics
- Clinical Trials
Background:
- Multiple sclerosis (MS) patients experience unpredictable relapses, complicating clinical trial data analysis.
- Modeling MS relapse data is challenging due to within-subject dependence and between-patient heterogeneity.
Purpose of the Study:
- To compare six statistical methods for assessing treatment efficacy in reducing MS relapse frequency.
- To evaluate methods based on Poisson-type regressions and Cox proportional hazards models.
Main Methods:
- Comparison of six statistical models, including Poisson-type regressions and Cox proportional hazards models.
- Application of models to Tysabri (Natalizumab) multiple sclerosis trial data.
- Analysis performed using SAS statistical software.
Main Results:
- Different statistical models yielded varying results based on their underlying assumptions.
- Negative binomial regression demonstrated simplicity and efficiency for evaluating overall treatment effects.
Conclusions:
- Negative binomial regression is recommended for multiple sclerosis clinical trials evaluating treatment efficacy on relapse frequency.
- The choice of statistical method impacts the interpretation of treatment effects in MS studies.
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