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Motor Neurone Disease: Basic Designs, Sample Sizes and Pitfalls

  • B. Rix Brooks
  • M. Sanjak
  • D. Belden
  • A. Waclawik
Chapter

Abstract

In the last quarter-century the assault on motor neurone disease (MND)/amyotrophic lateral sclerosis (ALS) was initiated by single-center small clinical trials with technically complex, not widely validated outcome measurements as multiple endpoints. Over time there was a steady escalation to multicenter large, sometimes international, clinical trials with one simple, easily and precisely determined outcome measurement as the primary endpoint. During this evolution, it became apparent that there was an imbalance in our understanding of the outcome measures employed in determining the potential treatment effects of interventions for MND/ALS. Foremost was the unclear relationship between standard clinical measures of muscle strength by manual muscle testing and computerized isometric muscle strength in MND/ALS patients. Furthermore, despite reproducibiity and accuracy testing of disease-specific clinimetric scales in MND/ALS patients with validity testing against standard general purpose disability scales, few studies compared different scales in the same MND/ALS patient population or appraised the properties of these clinimetric scales over time [1–14]. Both large and small clinical trials have employed analytic methods including simple descriptive statistics of the outcome measure with repeated measures, change from baseline or slope measurements. The controversial evaluation of the anti-glutamate drug, riluzole, employed a simple trial design with survival as the clinical endpoint [15–19]. This clinical trial design and analysis with event history (time-to-failure or survival) statistics highlighted the potential importance of study design.

Keywords

Force Vital Capacity Percutaneous Endoscopic Gastrostomy Motor Neurone Disease Maximum Voluntary Isometric Contraction Sickness Impact Profile 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag London 2001

Authors and Affiliations

  • B. Rix Brooks
  • M. Sanjak
  • D. Belden
  • A. Waclawik

There are no affiliations available

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