Journal article
Sample size estimates for determining treatment effects in high-risk patients with early relapsing-remitting multiple sclerosis
Multiple sclerosis, Vol.9(3), pp.289-292
Jun 2003
PMID: 12814177
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
Background: Risk factors for short-term progression in early relapsing-remitting MS have been identified recently. Previously we determined potential risk factors for rapid progression of early relapsing-remitting MS and identified three groups of high-risk patients. These non-mutually exclusive groups of patients were drawn from a consecutively studied sample of 98 patients with newly diagnosed MS. High-risk patients had a history of either poor recovery from initial attacks, more than two attacks in the first two years of disease, or a combination of at least four other risk factors. Objective: To determine differences in sample sizes required to show a meaningful treatment effect when using a high-risk sample versus a random sample of patients. Methods: Power analyses were used to calculate the different sample sizes needed for hypothetical treatment trials. Results: We found that substantially smaller numbers of patients should be needed to show a significant treatment effect by employing these high-risk groups of patients as compared to a random population of MS patients (e.g., 58% reduction in sample size in one model). Conclusion: The use of patients at higher risk of progression to perform drug treatment trials can be considered as a means to reduce the number of patients needed to show a significant treatment effect for patients with very early MS.
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Details
- Title
- Sample size estimates for determining treatment effects in high-risk patients with early relapsing-remitting multiple sclerosis
- Creators
- Thomas F Scott - Drexel UniversityCarol J Schramke - Drexel UniversityGary Cutter - University of Nevada Reno
- Publication Details
- Multiple sclerosis, Vol.9(3), pp.289-292
- Publisher
- SAGE Publications
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Neurology
- Identifiers
- 991019167436904721
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- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Clinical Neurology
- Neurosciences