The relative efficiency of time‐to‐progression and continuous measures of cognition in presymptomatic Alzheimer's disease
Abstract Introduction Clinical trials on preclinical Alzheimer's disease are challenging because of the slow rate of disease progression. We use a simulation study to demonstrate that models of repeated cognitive assessments detect treatment effects more efficiently than models of time to progr...
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| Format: | Article |
| Language: | English |
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Wiley
2019-01-01
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| Series: | Alzheimer’s & Dementia: Translational Research & Clinical Interventions |
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| Online Access: | https://doi.org/10.1016/j.trci.2019.04.004 |
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| author | Dan Li Samuel Iddi Paul S. Aisen Wesley K. Thompson Michael C. Donohue Alzheimer's Disease Neuroimaging Initiative |
| author_facet | Dan Li Samuel Iddi Paul S. Aisen Wesley K. Thompson Michael C. Donohue Alzheimer's Disease Neuroimaging Initiative |
| author_sort | Dan Li |
| collection | DOAJ |
| description | Abstract Introduction Clinical trials on preclinical Alzheimer's disease are challenging because of the slow rate of disease progression. We use a simulation study to demonstrate that models of repeated cognitive assessments detect treatment effects more efficiently than models of time to progression. Methods Multivariate continuous data are simulated from a Bayesian joint mixed‐effects model fit to data from the Alzheimer's Disease Neuroimaging Initiative. Simulated progression events are algorithmically derived from the continuous assessments using a random forest model fit to the same data. Results We find that power is approximately doubled with models of repeated continuous outcomes compared with the time‐to‐progression analysis. The simulations also demonstrate that a plausible informative missing data pattern can induce a bias that inflates treatment effects, yet 5% type I error is maintained. Discussion Given the relative inefficiency of time to progression, it should be avoided as a primary analysis approach in clinical trials of preclinical Alzheimer's disease. |
| format | Article |
| id | doaj-art-91c05e03b761481caeff8de05b3e95d5 |
| institution | DOAJ |
| issn | 2352-8737 |
| language | English |
| publishDate | 2019-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Alzheimer’s & Dementia: Translational Research & Clinical Interventions |
| spelling | doaj-art-91c05e03b761481caeff8de05b3e95d52025-08-20T03:21:59ZengWileyAlzheimer’s & Dementia: Translational Research & Clinical Interventions2352-87372019-01-015130831810.1016/j.trci.2019.04.004The relative efficiency of time‐to‐progression and continuous measures of cognition in presymptomatic Alzheimer's diseaseDan Li0Samuel Iddi1Paul S. Aisen2Wesley K. Thompson3Michael C. Donohue4Alzheimer's Disease Neuroimaging Initiative5Alzheimer's Therapeutic Research Institute, Keck School of Medicine, University of Southern CaliforniaSan DiegoCAUSAAlzheimer's Therapeutic Research Institute, Keck School of Medicine, University of Southern CaliforniaSan DiegoCAUSAAlzheimer's Therapeutic Research Institute, Keck School of Medicine, University of Southern CaliforniaSan DiegoCAUSADepartment of PsychiatryUniversity of CaliforniaSan DiegoCAUSAAlzheimer's Therapeutic Research Institute, Keck School of Medicine, University of Southern CaliforniaSan DiegoCAUSAAlzheimer's Therapeutic Research Institute, Keck School of Medicine, University of Southern CaliforniaSan DiegoCAUSAAbstract Introduction Clinical trials on preclinical Alzheimer's disease are challenging because of the slow rate of disease progression. We use a simulation study to demonstrate that models of repeated cognitive assessments detect treatment effects more efficiently than models of time to progression. Methods Multivariate continuous data are simulated from a Bayesian joint mixed‐effects model fit to data from the Alzheimer's Disease Neuroimaging Initiative. Simulated progression events are algorithmically derived from the continuous assessments using a random forest model fit to the same data. Results We find that power is approximately doubled with models of repeated continuous outcomes compared with the time‐to‐progression analysis. The simulations also demonstrate that a plausible informative missing data pattern can induce a bias that inflates treatment effects, yet 5% type I error is maintained. Discussion Given the relative inefficiency of time to progression, it should be avoided as a primary analysis approach in clinical trials of preclinical Alzheimer's disease.https://doi.org/10.1016/j.trci.2019.04.004Clinical trial simulationsAlzheimer's diseaseCox proportional hazards modelLongitudinal dataMixed model of repeated measures (MMRM)Statistical power |
| spellingShingle | Dan Li Samuel Iddi Paul S. Aisen Wesley K. Thompson Michael C. Donohue Alzheimer's Disease Neuroimaging Initiative The relative efficiency of time‐to‐progression and continuous measures of cognition in presymptomatic Alzheimer's disease Alzheimer’s & Dementia: Translational Research & Clinical Interventions Clinical trial simulations Alzheimer's disease Cox proportional hazards model Longitudinal data Mixed model of repeated measures (MMRM) Statistical power |
| title | The relative efficiency of time‐to‐progression and continuous measures of cognition in presymptomatic Alzheimer's disease |
| title_full | The relative efficiency of time‐to‐progression and continuous measures of cognition in presymptomatic Alzheimer's disease |
| title_fullStr | The relative efficiency of time‐to‐progression and continuous measures of cognition in presymptomatic Alzheimer's disease |
| title_full_unstemmed | The relative efficiency of time‐to‐progression and continuous measures of cognition in presymptomatic Alzheimer's disease |
| title_short | The relative efficiency of time‐to‐progression and continuous measures of cognition in presymptomatic Alzheimer's disease |
| title_sort | relative efficiency of time to progression and continuous measures of cognition in presymptomatic alzheimer s disease |
| topic | Clinical trial simulations Alzheimer's disease Cox proportional hazards model Longitudinal data Mixed model of repeated measures (MMRM) Statistical power |
| url | https://doi.org/10.1016/j.trci.2019.04.004 |
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