D-optimal designs for multiarm trials with dropouts
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 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.
available case analysis, design of experiments, linear mixed models, noninformative dropouts
2749-2766
Lee, Kim May
a06e1f03-c653-48f2-b79c-05365ad33938
Biedermann, Stefanie
fe3027d2-13c3-4d9a-bfef-bcc7c6415039
Mitra, Robin
2b944cd7-5be8-4dd1-ab44-f8ada9a33405
10 July 2019
Lee, Kim May
a06e1f03-c653-48f2-b79c-05365ad33938
Biedermann, Stefanie
fe3027d2-13c3-4d9a-bfef-bcc7c6415039
Mitra, Robin
2b944cd7-5be8-4dd1-ab44-f8ada9a33405
Lee, Kim May, Biedermann, Stefanie and Mitra, Robin
(2019)
D-optimal designs for multiarm trials with dropouts.
Statistics in Medicine, 38 (15), .
(doi:10.1002/sim.8148).
Abstract
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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Lee et al 2019 Statistics in Medicine
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Submitted date: 25 March 2017
Accepted/In Press date: 28 February 2019
e-pub ahead of print date: 25 March 2019
Published date: 10 July 2019
Keywords:
available case analysis, design of experiments, linear mixed models, noninformative dropouts
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Statistics
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Local EPrints ID: 407748
URI: http://eprints.soton.ac.uk/id/eprint/407748
ISSN: 0277-6715
PURE UUID: e3741197-c2e2-4863-8556-3754bd8b7d0b
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Date deposited: 25 Apr 2017 01:06
Last modified: 16 Mar 2024 03:51
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Kim May Lee
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