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D‐optimal designs for multiarm trials with dropouts

Multiarm trials with follow‐up on participants are commonly implemented to assess treatment effects on a population over the course of the studies. Dropout is an unavoidable issue especially when the duration of the multiarm study is long. Its impact is often ignored at the design stage, which may l...

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Detalles Bibliográficos
Autores principales: Lee, Kim May, Biedermann, Stefanie, Mitra, Robin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6563492/
https://www.ncbi.nlm.nih.gov/pubmed/30912173
http://dx.doi.org/10.1002/sim.8148
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author Lee, Kim May
Biedermann, Stefanie
Mitra, Robin
author_facet Lee, Kim May
Biedermann, Stefanie
Mitra, Robin
author_sort Lee, Kim May
collection PubMed
description Multiarm trials with follow‐up on participants are commonly implemented to assess treatment effects on a population over the course of the studies. Dropout is an unavoidable issue especially when the duration of the multiarm study is long. Its impact is often ignored at the design stage, which may lead to less accurate statistical conclusions. We develop an optimal design framework for trials with repeated measurements, which takes potential dropouts into account, and we provide designs for linear mixed models where the presence of dropouts is noninformative and dependent on design variables. Our framework is illustrated through redesigning a clinical trial on Alzheimer's disease, whereby the benefits of our designs compared with standard designs are demonstrated through simulations.
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spelling pubmed-65634922019-06-17 D‐optimal designs for multiarm trials with dropouts Lee, Kim May Biedermann, Stefanie Mitra, Robin Stat Med Research Articles Multiarm trials with follow‐up on participants are commonly implemented to assess treatment effects on a population over the course of the studies. Dropout is an unavoidable issue especially when the duration of the multiarm study is long. Its impact is often ignored at the design stage, which may lead to less accurate statistical conclusions. We develop an optimal design framework for trials with repeated measurements, which takes potential dropouts into account, and we provide designs for linear mixed models where the presence of dropouts is noninformative and dependent on design variables. Our framework is illustrated through redesigning a clinical trial on Alzheimer's disease, whereby the benefits of our designs compared with standard designs are demonstrated through simulations. John Wiley and Sons Inc. 2019-03-25 2019-07-10 /pmc/articles/PMC6563492/ /pubmed/30912173 http://dx.doi.org/10.1002/sim.8148 Text en © 2019 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd. This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Articles
Lee, Kim May
Biedermann, Stefanie
Mitra, Robin
D‐optimal designs for multiarm trials with dropouts
title D‐optimal designs for multiarm trials with dropouts
title_full D‐optimal designs for multiarm trials with dropouts
title_fullStr D‐optimal designs for multiarm trials with dropouts
title_full_unstemmed D‐optimal designs for multiarm trials with dropouts
title_short D‐optimal designs for multiarm trials with dropouts
title_sort d‐optimal designs for multiarm trials with dropouts
topic Research Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6563492/
https://www.ncbi.nlm.nih.gov/pubmed/30912173
http://dx.doi.org/10.1002/sim.8148
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