Short-Term Relapse Quantitation as a Fully Surrogate Endpoint for Long-Term Sustained Progression of Disability in RRMS Patients Treated with Natalizumab
Author(s) -
Y. C. Wang,
Alfred Sandrock,
J Richert,
Laura J. Meyerson,
Xiaoping Miao
Publication year - 2011
Publication title -
neurology research international
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.365
H-Index - 31
eISSN - 2090-1852
pISSN - 2090-1860
DOI - 10.1155/2011/195831
Subject(s) - natalizumab , medicine , surrogate endpoint , expanded disability status scale , multiple sclerosis , clinical trial , clinical endpoint , statistical significance , oncology , physical therapy , psychiatry
Time to sustained worsening in the expanded disability status scale as the standard for evaluating the accumulation of disability has been used as a measure of clinical efficacy in many relapsing-remitting multiple sclerosis (RRMS) clinical trials. However, this measurement usually requires a large sample and long-term study to demonstrate the treatment effect. Annualized relapse rate or time to first relapse is also widely used as alternative measurements of clinical efficacy. A formal statistical validation of short-term relapse activity as a surrogate endpoint for long-term sustained progression of disability could potentially permit smaller, shorter, and less expensive clinical trials in RRMS. Four statistical validation/evaluation approaches consistently showed that relapse activity through one year of treatment serves as statistically valid surrogate endpoint for time to sustained progression of disability. The analysis demonstrates that long-term sustained progression of disability can be predicted by short-term relapse measures with 4 consistent validations of statistical approaches, including a formal statistical hypothesis test. This was demonstrated in a large phase III trial of natalizumab and showed that the beneficial clinical effect of natalizumab on sustained progression of disability at 2 years in patients with RRMS can be predicted by the total number of relapses at 1 year.
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