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Cited by 99Dementia and Cognitive Impairment ResearchStatistical Methods in Clinical TrialsAlzheimer's disease research and treatments

Use of High-Frequency In-Home Monitoring Data May Reduce Sample Sizes Needed in Clinical Trials

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Dodge, Hiroko H., Zhu, Jian, Mattek, Nora C., Austin, Daniel, Kornfeld, Judith, Kaye, Jeffrey A.·2015·PLOS ONE
DOI 10.1371/journal.pone.0138095PMC PMC4574479
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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.

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  • Digital biomarkers used in clinical trials are capable of significantly reducing needed sample sizes.

    “digital biomarkers used in clinical trials that are capable of significantly reducing needed sample sizes. 39 , 40”

    From Developing digital health technologies for frontotemporal degeneration (2025) · cites this paper as [40]

    The Results (‘Sample sizes needed to achieve 80% power’; page not provided) estimate much smaller required samples using in-home activity measures than using annual memory tests. But these are estimates for proposed Alzheimer’s trials, not a demonstrated reduction in clinical trials; the Discussion (‘Cost’; page not provided) says sample sizes ‘may be reduced.’ The claim presents that potential more definitively and broadly than the paper does. (LLM, conf=0.84)

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  1. [1]DiMasi JA, Hansen RW, Grabowski HG. The price of innovation: new estimates of drug development costs. J Health Econ 2003; 22: 151– 185. 12606142
  2. [2]Paul SM, Mytelka DS, Dunwiddie CT, Persinger CC, Munos BH, Lindborg SR, et al How to improve R&D productivity: the pharmaceutical industry's grand challenge. Nature reviews Drug discovery 2010; 9: 203– 214. 10.1038/nrd3078 20168317 · DOI · Paper page
  3. [3]Ard MC, Edland SD. Power calculations for clinical trials in Alzheimer's disease. Journal of Alzheimer's disease: JAD 2011; 26 Suppl 3: 369– 377. 10.3233/JAD-2011-0062 21971476 PMC3568927 · DOI
  4. [5]Dorsey ER, Venuto C, Venkataraman V, Harris DA, Kieburtz K. Novel methods and technologies for 21st-century clinical trials: a review. JAMA Neurol 2015; 72: 582– 588. 10.1001/jamaneurol.2014.4524 25730665 PMC4708881 · DOI · Paper page
  5. [6]Aisen PS, Andrieu S, Sampaio C, Carrillo M, Khachaturian ZS, Dubois B, et al Report of the task force on designing clinical trials in early (predementia) AD. Neurology 2011; 76: 280– 286. 10.1212/WNL.0b013e318207b1b9 21178097 PMC3034393 · DOI
  6. [7]Vellas B, Bateman R, Blennow K, Frisoni G, Johnson K, Katz R, et al Endpoints for Pre-Dementia AD Trials: A Report from the EU/US CTAD Task Force. The Journal of Prevention of Alzheimer’s Disease 2015; 2: 128– 135. 26247004 PMC4523051
  7. [8]Sperling RA, Rentz DM, Johnson KA, Karlawish J, Donohue M, Salmon DP, et al The A4 study: stopping AD before symptoms begin? Science translational medicine 2014; 6: 228fs213. PMC4049292 24648338
  8. [9]Mills SM, Mallmann J, Santacruz AM, Fuqua A, Carril M, Aisen PS, et al Preclinical trials in autosomal dominant AD: implementation of the DIAN-TU trial. Rev Neurol (Paris) 2013; 169: 737– 743. 24016464 PMC3880800
  9. [10]Reiman EM, Langbaum JB, Fleisher AS, Caselli RJ, Chen K, Ayutyanont N, et al Alzheimer's Prevention Initiative: a plan to accelerate the evaluation of presymptomatic treatments. Journal of Alzheimer's disease: JAD 2011; 26 Suppl 3: 321– 329. 10.3233/JAD-2011-0059 21971471 PMC3343739 · DOI
  10. [11]Rowe CC, Ellis KA, Rimajova M, Bourgeat P, Pike KE, Jones G, et al Amyloid imaging results from the Australian Imaging, Biomarkers and Lifestyle (AIBL) study of aging. Neurobiol Aging 2010; 31: 1275– 1283. 10.1016/j.neurobiolaging.2010.04.007 20472326 · DOI · Paper page
  11. [12]Leoutsakos JM, Bartlett AL, Forrester SN, Lyketsos CG. Simulating effects of biomarker enrichment on Alzheimer's disease prevention trials: conceptual framework and example. Alzheimers Dement 2014; 10: 152– 161. 10.1016/j.jalz.2013.05.1776 23954029 PMC3925200 · DOI
  12. [13]Donohue MC, Sperling RA, Salmon DP, Rentz DM, Raman R, Thomas RG, et al The preclinical Alzheimer cognitive composite: measuring amyloid-related decline. JAMA Neurol 2014; 71: 961– 970. 10.1001/jamaneurol.2014.803 24886908 PMC4439182 · DOI · Paper page
  13. [14]Kaye JA, Maxwell SA, Mattek N, Hayes TL, Dodge H, Pavel M, et al Intelligent systems for assessing aging changes: home-based, unobtrusive, and continuous assessment of aging. J Gerontol B Psychol Sci Soc Sci 2011; 66 Suppl 1: i180– i190. 10.1093/geronb/gbq095 21743050 PMC3132763 · DOI
  14. [15]Dodge HH, Mattek NC, Austin D, Hayes TL, Kaye JA. In-home walking speeds and variability trajectories associated with mild cognitive impairment. Neurology 2012; 78: 1946– 1952. 10.1212/WNL.0b013e318259e1de 22689734 PMC3369505 · DOI
  15. [16]Kaye J, Mattek N, Dodge HH, Campbell I, Hayes T, Austin D, et al Unobtrusive measurement of daily computer use to detect mild cognitive impairment. Alzheimers Dement 2013; 10: 10– 17. 10.1016/j.jalz.2013.01.011 23688576 PMC3872486 · DOI · Paper page
  16. [17]Folstein MF, Folstein SE, McHugh PR. "Mini-mental state". A practical method for grading the cognitive state of patients for the clinician. J Psychiatr Res 1975; 12: 189– 198. 1202204
  17. [18]Morris JC. The Clinical Dementia Rating (CDR): current version and scoring rules. Neurology 1993; 43: 2412– 2414. 8232972
  18. [19]Hayes TL, Abendroth F, Adami A, Pavel M, Zitzelberger TA, Kaye JA. Unobtrusive assessment of activity patterns associated with mild cognitive impairment. Alzheimers Dement 2008; 4: 395– 405. 10.1016/j.jalz.2008.07.004 19012864 PMC2664300 · DOI · Paper page
  19. [20]Hagler S, Austin D, Hayes TL, Kaye J, Pavel M. Unobtrusive and ubiquitous in-home monitoring: a methodology for continuous assessment of gait velocity in elders. IEEE Trans Biomed Eng 2010; 57: 813– 820. 10.1109/TBME.2009.2036732 19932989 PMC2895023 · DOI
  20. [22]Lezak MD, Howieson DB, Bigler ED, Tranel D. Neuropsychological Asessment. New York: Oxford University Press 2012.
  21. [23]Reitan RM. Validity of the Trail-making Tests as an indication of organic brain damage. Percept Mot Skills 1985; 8: 271– 276.
  22. [26]Yi Q, Panzarella T. Estimating sample size for tests on trends across repeated measurements with missing data based on the interaction term in a mixed model. Control Clin Trials 2002; 23: 481– 496. 12392862
  23. [27]Hayes TL, Larimer N, Adami A, Kaye JA. Medication adherence in healthy elders: small cognitive changes make a big difference. J Aging Health 2009; 21: 567– 580. 10.1177/0898264309332836 19339680 PMC2910516 · DOI · Paper page
  24. [28]Thielke SM, Mattek NC, Hayes TL, Dodge HH, Quinones AR, Austin D, et al Associations between observed in-home behaviors and self-reported low mood in community-dwelling older adults. J Am Geriatr Soc 2014; 62: 685– 689. 10.1111/jgs.12744 24635020 PMC4007277 · DOI
  25. [29]Petersen J, Austin D, Kaye JA, Pavel M, Hayes TL. Unobtrusive In-Home Detection of Time Spent Out-of-Home With Applications to Loneliness and Physical Activity. IEEE journal of biomedical and health informatics 2014; 18: 1590– 1596. 10.1109/JBHI.2013.2294276 25192570 PMC4159765 · DOI
  26. [30]Lyons BE, Austin D, Seelye A, Petersen J, Yeargers J, Riley T, et al Pervasive Computing Technologies to Continuously Assess Alzheimer's Disease Progression and Intervention Efficacy. Frontiers in aging neuroscience 2015; 7: 102 10.3389/fnagi.2015.00102 26113819 PMC4462097 · DOI
  27. [31]Dodge HH, Mattek N, Gregor M, Bowman M, Seelye A, Ybarra O, et al Social Markers of Mild Cognitive Impairment: Proportion of Word Counts in Free Conversational Speech. Current Alzheimer Research 2015; 12: 513– 519. 26027814 PMC4526336
  28. [32]Hayes TL, Riley T, Mattek N, Pavel M, Kaye JA. Sleep habits in mild cognitive impairment. Alzheimer Dis Assoc Disord 2014; 28: 145– 150. 10.1097/WAD.0000000000000010 24145694 PMC3990656 · DOI
  29. [33]Katz R. Biomarkers and surrogate markers: an FDA perspective. NeuroRx: the journal of the American Society for Experimental NeuroTherapeutics 2004; 1: 189– 195. 15717019 PMC534924
  30. [34]Verghese J, Robbins M, Holtzer R, Zimmerman M, Wang C, Xue X, et al Gait dysfunction in mild cognitive impairment syndromes. J Am Geriatr Soc 2008; 56: 1244– 1251. 10.1111/j.1532-5415.2008.01758.x 18482293 PMC2574944 · DOI
  31. [35]Camicioli R, Howieson D, Lehman S, Kaye J. Talking while walking: the effect of a dual task in aging and Alzheimer's disease. Neurology 1997; 48: 955– 958. 9109884
  32. [36]Camicioli R, Howieson D, Oken B, Sexton G, Kaye J. Motor slowing precedes cognitive impairment in the oldest old. Neurology 1998; 50: 1496– 1498. 9596020
  33. [37]Buracchio T, Dodge HH, Howieson D, Wasserman D, Kaye J. The trajectory of gait speed preceding mild cognitive impairment. Arch Neurol 2010; 67: 980– 986. 10.1001/archneurol.2010.159 20697049 PMC2921227 · DOI
  34. [38]Caroli A, Prestia A, Wade S, Chen K, Ayutyanont N, Landau SM, et al Alzheimer Disease Biomarkers as Outcome Measures for Clinical Trials in MCI. Alzheimer Dis Assoc Disord 2015; 29: 101– 109. 10.1097/WAD.0000000000000071 25437302 PMC4437812 · DOI
  35. [39]Dodge HH, Zhu J, Harvey D, Saito N, Silbert LC, Kaye JA, et al Biomarker progressions explain higher variability in stage-specific cognitive decline than baseline values in Alzheimer disease. Alzheimers Dement 2014; 10: 690– 703. 10.1016/j.jalz.2014.04.513 25022534 PMC4253728 · DOI
  36. [40]Schneider LS, Kennedy RE, Cutter GR, Alzheimer's Disease Neuroimaging I. Requiring an amyloid-beta1-42 biomarker for prodromal Alzheimer's disease or mild cognitive impairment does not lead to more efficient clinical trials. Alzheimers Dement 2010; 6: 367– 377. 10.1016/j.jalz.2010.07.004 20813339 PMC2947209 · DOI

Cited by

Sorted by citations

Authors
Dodge, H. H., Zhu, J., Mattek, N. C., Austin, D., Kornfeld, J., & Kaye, J. A.
Affiliations
  • Oregon Health & Science University (US)
  • University of Michigan (US)
Corresponding author
Dodge, Hiroko H.
ORCID
Venue
PLOS ONE
Publisher
Public Library of Science (PLoS)
Published
2015-09-17
Volume
10
Issue
9
Pages
e0138095
ISSN
1932-6203 (ISSN-L)
DOI
10.1371/journal.pone.0138095
PMC
PMC4574479
OpenAlex
W2266461367
Cited by
99
References
2
Citations by year
2026: 7 · 2025: 4 · 2024: 14 · 2023: 13 · 2022: 12 · 2021: 8 · 2020: 16 · 2019: 11 · 2018: 5 · 2017: 7 · 2016: 2
FWCI
3.79
Type
journal article
Language
en
Open access
Licence
Locations
  • PLoS ONE (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • Figshare (submittedVersion, cc-by)
Funders
  • National Institutes of Health (AG008017, AG024978)
  • National Institute on Aging (R01AG024059, P30AG008017, P30AG024978, R01AG033581)
Topics
  • Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
  • Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
  • Health Sciences › Medicine › Physiology › Alzheimer's disease research and treatments
Keywords
sample size determination, Alzheimer's disease, mild cognitive impairment, functional performance, walking speed, computer use, cognitive decline trajectories, statistical power, asymptomatic individuals
MeSH terms
Aged, 80 and over, Alzheimer Disease, Clinical Trials as Topic, Female, Humans, Male, Neuropsychological Tests, Biomarkers, Amyloid beta-Peptides, Sample Size, Disease Progression, Monitoring, Ambulatory, Cognitive Dysfunction

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