title: Use of High-Frequency In-Home Monitoring Data May Reduce Sample Sizes Needed in Clinical Trials.
abstract: BACKGROUND
  Trials in Alzheimer's disease are increasingly focusing on prevention in asymptomatic individuals. This poses a challenge in examining treatment effects since currently available approaches are often unable to detect cognitive and functional changes among asymptomatic individuals. Resultant small effect sizes require large sample sizes using biomarkers or secondary measures for randomized controlled trials (RCTs). Better assessment approaches and outcomes capable of capturing subtle changes during asymptomatic disease stages are needed.
  
  
  OBJECTIVE
  We aimed to develop a new approach to track changes in functional outcomes by using individual-specific distributions (as opposed to group-norms) of unobtrusive continuously monitored in-home data. Our objective was to compare sample sizes required to achieve sufficient power to detect prevention trial effects in trajectories of outcomes in two scenarios: (1) annually assessed neuropsychological test scores (a conventional approach), and (2) the likelihood of having subject-specific low performance thresholds, both modeled as a function of time.
  
  
  METHODS
  One hundred nineteen cognitively intact subjects were enrolled and followed over 3 years in the Intelligent Systems for Assessing Aging Change (ISAAC) study. Using the difference in empirically identified time slopes between those who remained cognitively intact during follow-up (normal control, NC) and those who transitioned to mild cognitive impairment (MCI), we estimated comparative sample sizes required to achieve up to 80% statistical power over a range of effect sizes for detecting reductions in the difference in time slopes between NC and MCI incidence before transition.
  
  
  RESULTS
  Sample size estimates indicated approximately 2000 subjects with a follow-up duration of 4 years would be needed to achieve a 30% effect size when the outcome is an annually assessed memory test score. When the outcome is likelihood of low walking speed defined using the individual-specific distributions of walking speed collected at baseline, 262 subjects are required. Similarly for computer use, 26 subjects are required.
  
  
  CONCLUSIONS
  Individual-specific thresholds of low functional performance based on high-frequency in-home monitoring data distinguish trajectories of MCI from NC and could substantially reduce sample sizes needed in dementia prevention RCTs.
authors.affiliation: Department of Neurology, Layton Aging and Alzheimer's Disease Center, Oregon Health & Science University, Portland, Oregon, United States of America; Oregon Center for Aging and Technology (ORCATECH), Oregon Health & Science University, Portland, Oregon, United States of America; Department of Neurology, Michigan Alzheimer's Disease Center, University of Michigan, Ann Arbor, Michigan, United States of America.
authors.forename: Hiroko H
authors.initials: HH
authors.lastname: Dodge
authors.affiliation: Graduate School of Public Health, University of Michigan, Ann Arbor, Michigan, United States of America.
authors.forename: Jian
authors.initials: J
authors.lastname: Zhu
authors.affiliation: Department of Neurology, Layton Aging and Alzheimer's Disease Center, Oregon Health & Science University, Portland, Oregon, United States of America; Oregon Center for Aging and Technology (ORCATECH), Oregon Health & Science University, Portland, Oregon, United States of America.
authors.forename: Nora C
authors.initials: NC
authors.lastname: Mattek
authors.affiliation: Department of Neurology, Layton Aging and Alzheimer's Disease Center, Oregon Health & Science University, Portland, Oregon, United States of America; Oregon Center for Aging and Technology (ORCATECH), Oregon Health & Science University, Portland, Oregon, United States of America.
authors.forename: Daniel
authors.initials: D
authors.lastname: Austin
authors.affiliation: Department of Neurology, Layton Aging and Alzheimer's Disease Center, Oregon Health & Science University, Portland, Oregon, United States of America; Oregon Center for Aging and Technology (ORCATECH), Oregon Health & Science University, Portland, Oregon, United States of America.
authors.forename: Judith
authors.initials: J
authors.lastname: Kornfeld
authors.affiliation: Department of Neurology, Layton Aging and Alzheimer's Disease Center, Oregon Health & Science University, Portland, Oregon, United States of America; Oregon Center for Aging and Technology (ORCATECH), Oregon Health & Science University, Portland, Oregon, United States of America; Portland Veterans Medical Center, Portland, Oregon, United States of America.
authors.forename: Jeffrey A
authors.initials: JA
authors.lastname: Kaye
mesh_terms: D000369:Aged, 80 and over; D000544:Alzheimer Disease / Q000175:diagnosis* / Q000188:drug therapy / Q000517:prevention & control; D016229:Amyloid beta-Peptides / Q000378:metabolism; D015415:Biomarkers / Q000032:analysis; D002986:Clinical Trials as Topic / Q000379:methods*; D060825:Cognitive Dysfunction / Q000175:diagnosis / Q000503:physiopathology*; D018450:Disease Progression; D005260:Female; D006801:Humans; D008297:Male; D018670:Monitoring, Ambulatory / Q000379:methods*; D009483:Neuropsychological Tests / Q000706:statistics & numerical data*; D018401:Sample Size
chemical_list: D016229:Amyloid beta-Peptides; D015415:Biomarkers