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Proof of concept demonstration of optimal composite MRI endpoints for clinical trials
INTRODUCTION: Atrophy measures derived from structural MRI are promising outcome measures for early phase clinical trials, especially for rare diseases such as primary progressive aphasia (PPA), where the small available subject pool limits our ability to perform meaningfully powered trials with tra...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5363955/ https://www.ncbi.nlm.nih.gov/pubmed/28345017 http://dx.doi.org/10.1016/j.trci.2016.05.002 |
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author | Edland, Steven D. Ard, M. Colin Sridhar, Jaiashre Cobia, Derin Martersteck, Adam Mesulam, M.-Marsel Rogalski, Emily J. |
author_facet | Edland, Steven D. Ard, M. Colin Sridhar, Jaiashre Cobia, Derin Martersteck, Adam Mesulam, M.-Marsel Rogalski, Emily J. |
author_sort | Edland, Steven D. |
collection | PubMed |
description | INTRODUCTION: Atrophy measures derived from structural MRI are promising outcome measures for early phase clinical trials, especially for rare diseases such as primary progressive aphasia (PPA), where the small available subject pool limits our ability to perform meaningfully powered trials with traditional cognitive and functional outcome measures. METHODS: We investigated a composite atrophy index in 26 PPA participants with longitudinal MRIs separated by 2 years. Rogalski et al.[5] previously demonstrated that atrophy of the left perisylvian temporal cortex (PSTC) is a highly sensitive measure of disease progression in this population and a promising endpoint for clinical trials. Using methods described by Ard et al.[1], we constructed a composite atrophy index composed of a weighted sum of volumetric measures of 10 regions of interest within the left perisylvian cortex using weights that maximize signal-to-noise and minimize sample size required of trials using the resulting score. Sample size required to detect a fixed percentage slowing in atrophy in a 2-year clinical trial with equal allocation of subjects across arms and 90% power was calculated for the PSTC and optimal composite surrogate biomarker endpoints. RESULTS: The optimal composite endpoint required 38% fewer subjects to detect the same percent slowing in atrophy than required by the left PSTC endpoint. CONCLUSIONS: Optimal composites can increase the power of clinical trials and increase the probability that smaller trials are informative, an observation especially relevant for PPA but also for related neurodegenerative disorders including Alzheimer's disease. |
format | Online Article Text |
id | pubmed-5363955 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-53639552017-09-01 Proof of concept demonstration of optimal composite MRI endpoints for clinical trials Edland, Steven D. Ard, M. Colin Sridhar, Jaiashre Cobia, Derin Martersteck, Adam Mesulam, M.-Marsel Rogalski, Emily J. Alzheimers Dement (N Y) Featured Article INTRODUCTION: Atrophy measures derived from structural MRI are promising outcome measures for early phase clinical trials, especially for rare diseases such as primary progressive aphasia (PPA), where the small available subject pool limits our ability to perform meaningfully powered trials with traditional cognitive and functional outcome measures. METHODS: We investigated a composite atrophy index in 26 PPA participants with longitudinal MRIs separated by 2 years. Rogalski et al.[5] previously demonstrated that atrophy of the left perisylvian temporal cortex (PSTC) is a highly sensitive measure of disease progression in this population and a promising endpoint for clinical trials. Using methods described by Ard et al.[1], we constructed a composite atrophy index composed of a weighted sum of volumetric measures of 10 regions of interest within the left perisylvian cortex using weights that maximize signal-to-noise and minimize sample size required of trials using the resulting score. Sample size required to detect a fixed percentage slowing in atrophy in a 2-year clinical trial with equal allocation of subjects across arms and 90% power was calculated for the PSTC and optimal composite surrogate biomarker endpoints. RESULTS: The optimal composite endpoint required 38% fewer subjects to detect the same percent slowing in atrophy than required by the left PSTC endpoint. CONCLUSIONS: Optimal composites can increase the power of clinical trials and increase the probability that smaller trials are informative, an observation especially relevant for PPA but also for related neurodegenerative disorders including Alzheimer's disease. Elsevier 2016-07-04 /pmc/articles/PMC5363955/ /pubmed/28345017 http://dx.doi.org/10.1016/j.trci.2016.05.002 Text en © 2016 The Authors http://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Featured Article Edland, Steven D. Ard, M. Colin Sridhar, Jaiashre Cobia, Derin Martersteck, Adam Mesulam, M.-Marsel Rogalski, Emily J. Proof of concept demonstration of optimal composite MRI endpoints for clinical trials |
title | Proof of concept demonstration of optimal composite MRI endpoints for clinical trials |
title_full | Proof of concept demonstration of optimal composite MRI endpoints for clinical trials |
title_fullStr | Proof of concept demonstration of optimal composite MRI endpoints for clinical trials |
title_full_unstemmed | Proof of concept demonstration of optimal composite MRI endpoints for clinical trials |
title_short | Proof of concept demonstration of optimal composite MRI endpoints for clinical trials |
title_sort | proof of concept demonstration of optimal composite mri endpoints for clinical trials |
topic | Featured Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5363955/ https://www.ncbi.nlm.nih.gov/pubmed/28345017 http://dx.doi.org/10.1016/j.trci.2016.05.002 |
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