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Optimizing clinical trial design for multiple system atrophy: lessons from the rifampicin study

期刊

CLINICAL AUTONOMIC RESEARCH
卷 25, 期 1, 页码 47-52

出版社

SPRINGER HEIDELBERG
DOI: 10.1007/s10286-015-0281-2

关键词

Multiple system atrophy; Rifampicin; Trial design

资金

  1. National Institutes of Health [NS 44233, U54 NS065736, K23NS075141]
  2. Mayo CTSA [UL1 TR000135]
  3. Kathy Shih Foundation
  4. Mayo Funds

向作者/读者索取更多资源

Multiple system atrophy (MSA) is a fatal neurodegenerative disorder characterized by autonomic failure and parkinsonism/ataxia; no treatment exists to slow disease progression. A number of factors have prevented or compromised trials targeting disease modification. A major hurdle has been uncertainty about the number of patients needed to achieve adequate power. Information based on natural history studies suggested such numbers to be so large that only international multi-center models seemed feasible. When designing the rifampicin trial in MSA we sought to identify and apply strategies that would improve power and reduce the number needed to treat to allow for an oligocenter approach. Strategies included: (1) inclusion/exclusion criteria designed to enroll patients with relatively early, actively progressing disease; (2) minimizing dropouts; (3) pre-defined interim analysis; and (4) approaches to reduce scoring variability. The model allowed for the number needed to treat to be only 50 patients per treatment arm. Ten selected sites managed to reach the recruitment goal within 12 months. The dropout rate was less than 10 %, and the goal of enrolling patients with actively progressing disease was accomplished as reflected by the progression rate in the placebo group. Data from this unfortunately negative trial can now be effectively used to more realistically power future trials. A number of ways to further improve trial design and feasibility have been identified and include rigorous site selection and training, designated primary site investigators, improved error trapping, early site visits, remedial training, and future biomarkers for earlier diagnosis and tracking of disease progression.

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