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D-optimal designs for multiarm trials with dropouts

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
0277-6715
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
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. (doi:10.1002/sim.8148).

Record type: Article

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.

Text
Lee et al 2019 Statistics in Medicine - Version of Record
Available under License Creative Commons Attribution.
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More information

Accepted/In Press date: 28 February 2019
e-pub ahead of print date: 25 March 2019
Keywords: available case analysis, design of experiments, linear mixed models, noninformative dropouts

Identifiers

Local EPrints ID: 429647
URI: https://eprints.soton.ac.uk/id/eprint/429647
ISSN: 0277-6715
PURE UUID: af923812-0c3d-457d-9347-c3fc1b08bd92
ORCID for Stefanie Biedermann: ORCID iD orcid.org/0000-0001-8900-8268

Catalogue record

Date deposited: 03 Apr 2019 16:30
Last modified: 27 Jul 2019 00:34

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