Use of High-Frequency In-Home Monitoring Data May Reduce Sample Sizes Needed in Clinical Trials.
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 sma...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2015-01-01
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| Series: | PLoS ONE |
| Online Access: | http://europepmc.org/articles/PMC4574479?pdf=render |
| _version_ | 1818893064762032128 |
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| author | Hiroko H Dodge Jian Zhu Nora C Mattek Daniel Austin Judith Kornfeld Jeffrey A Kaye |
| author_facet | Hiroko H Dodge Jian Zhu Nora C Mattek Daniel Austin Judith Kornfeld Jeffrey A Kaye |
| author_sort | Hiroko H Dodge |
| collection | DOAJ |
| description | 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.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.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.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.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. |
| first_indexed | 2024-12-19T18:06:40Z |
| format | Article |
| id | doaj.art-5b4a2fe8af554d248dbe5c7e23ad12aa |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-12-19T18:06:40Z |
| publishDate | 2015-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-5b4a2fe8af554d248dbe5c7e23ad12aa2022-12-21T20:11:27ZengPublic Library of Science (PLoS)PLoS ONE1932-62032015-01-01109e013809510.1371/journal.pone.0138095Use of High-Frequency In-Home Monitoring Data May Reduce Sample Sizes Needed in Clinical Trials.Hiroko H DodgeJian ZhuNora C MattekDaniel AustinJudith KornfeldJeffrey A KayeTrials 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.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.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.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.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.http://europepmc.org/articles/PMC4574479?pdf=render |
| spellingShingle | Hiroko H Dodge Jian Zhu Nora C Mattek Daniel Austin Judith Kornfeld Jeffrey A Kaye Use of High-Frequency In-Home Monitoring Data May Reduce Sample Sizes Needed in Clinical Trials. PLoS ONE |
| title | Use of High-Frequency In-Home Monitoring Data May Reduce Sample Sizes Needed in Clinical Trials. |
| title_full | Use of High-Frequency In-Home Monitoring Data May Reduce Sample Sizes Needed in Clinical Trials. |
| title_fullStr | Use of High-Frequency In-Home Monitoring Data May Reduce Sample Sizes Needed in Clinical Trials. |
| title_full_unstemmed | Use of High-Frequency In-Home Monitoring Data May Reduce Sample Sizes Needed in Clinical Trials. |
| title_short | Use of High-Frequency In-Home Monitoring Data May Reduce Sample Sizes Needed in Clinical Trials. |
| title_sort | use of high frequency in home monitoring data may reduce sample sizes needed in clinical trials |
| url | http://europepmc.org/articles/PMC4574479?pdf=render |
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