Boosting power for clinical trials using classifiers based on multiple biomarkers

Omid Kohannim, Xue Hua, Derrek P. Hibar, Suh Lee, Yi Yu Chou, Arthur W. Toga, Clifford R. Jack, Michael W. Weiner, Paul M. Thompson

Research output: Contribution to journalArticlepeer-review

128 Scopus citations


Machine learning methods pool diverse information to perform computer-assisted diagnosis and predict future clinical decline. We introduce a machine learning method to boost power in clinical trials. We created a Support Vector Machine algorithm that combines brain imaging and other biomarkers to classify 737 Alzheimer's disease Neuroimaging initiative (ADNI) subjects as having Alzheimer's disease (AD), mild cognitive impairment (MCI), or normal controls. We trained our classifiers based on example data including: MRI measures of hippocampal, ventricular, and temporal lobe volumes, a PET-FDG numerical summary, CSF biomarkers (t-tau, p-tau, and Aβ42), ApoE genotype, age, sex, and body mass index. MRI measures contributed most to Alzheimer's disease (AD) classification; PET-FDG and CSF biomarkers, particularly Aβ42, contributed more to MCI classification. Using all biomarkers jointly, we used our classifier to select the one-third of the subjects most likely to decline. In this subsample, fewer than 40 AD and MCI subjects would be needed to detect a 25% slowing in temporal lobe atrophy rates with 80% power-a substantial boosting of power relative to standard imaging measures.

Original languageEnglish (US)
Pages (from-to)1429-1442
Number of pages14
JournalNeurobiology of aging
Issue number8
StatePublished - Aug 2010


  • Alzheimer's disease
  • Biomarkers
  • Classification
  • Clinical trial enrichment
  • Magnetic resonance imaging
  • Mild cognitive impairment
  • Neuroimaging
  • Support vector machines

ASJC Scopus subject areas

  • Clinical Neurology
  • Geriatrics and Gerontology
  • Aging
  • General Neuroscience
  • Developmental Biology


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