Logo image
Sample size estimates for determining treatment effects in high-risk patients with early relapsing-remitting multiple sclerosis
Journal article   Peer reviewed

Sample size estimates for determining treatment effects in high-risk patients with early relapsing-remitting multiple sclerosis

Thomas F Scott, Carol J Schramke and Gary Cutter
Multiple sclerosis, v 9(3), pp 289-292
Jun 2003
PMID: 12814177

Abstract

risk factors prognosis sample size multiple sclerosis
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.

Metrics

12 Record Views
7 citations in Scopus

Details

UN Sustainable Development Goals (SDGs)

This publication has contributed to the advancement of the following goals:

#3 Good Health and Well-Being

InCites Highlights

Data related to this publication, from InCites Benchmarking & Analytics tool:

Collaboration types
Domestic collaboration
Web of Science research areas
Clinical Neurology
Neurosciences
Logo image