Literature DB >> 32658327

Statistical efficiency of patient data in randomized clinical trials of epilepsy treatments.

Juan Romero1, Daniel M Goldenholz1.   

Abstract

OBJECTIVE: Previous research suggests that natural fluctuations in seizure rates within individuals have a quantifiable impact on therapeutic clinical trial outcomes.
METHODS: A trial simulator estimated the statistical power of clinical trials with a typical trial design with and without patients included who exhibited a range of means (1-15 seizures/mo) and standard deviations (1-15 seizures/mo) in their baseline seizure rates. Trial outcomes were evaluated using 50% responder rates, median percentage change, and time to prerandomization.
RESULTS: Patients with higher seizure frequencies and lower standard deviations during their baseline contribute more to the statistical power regardless of the method used to evaluate the trial. Power varied from -20% to 30% depending on baseline seizure characteristics. SIGNIFICANCE: Patient-specific characteristics can predict the contributions to the statistical power of clinical trials for epilepsy treatments. It may be possible to characterize this contribution with baseline data, leading to more efficient clinical trials.
© 2020 International League Against Epilepsy.

Entities:  

Keywords:  clinical trials; deep learning; epilepsy; seizures; statistics

Year:  2020        PMID: 32658327     DOI: 10.1111/epi.16609

Source DB:  PubMed          Journal:  Epilepsia        ISSN: 0013-9580            Impact factor:   5.864


  1 in total

1.  Can machine learning improve randomized clinical trial analysis?

Authors:  Juan Romero; Sharon Chiang; Daniel M Goldenholz
Journal:  Seizure       Date:  2021-08-02       Impact factor: 3.414

  1 in total

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