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D-optimal designs for multi-arm trials with dropouts

D-optimal designs for multi-arm trials with dropouts
D-optimal designs for multi-arm trials with dropouts
Multi-arm 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 multi-arm 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 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.
missing Data, optimal design, dropout, longitudinal data
0277-6715
Lee, Kim
526cb8f4-17e8-40e3-881a-19e5eacabd23
Biedermann, Stefanie
fe3027d2-13c3-4d9a-bfef-bcc7c6415039
Mitra, Robin
2b944cd7-5be8-4dd1-ab44-f8ada9a33405
Lee, Kim
526cb8f4-17e8-40e3-881a-19e5eacabd23
Biedermann, Stefanie
fe3027d2-13c3-4d9a-bfef-bcc7c6415039
Mitra, Robin
2b944cd7-5be8-4dd1-ab44-f8ada9a33405

Lee, Kim, Biedermann, Stefanie and Mitra, Robin (2019) D-optimal designs for multi-arm trials with dropouts. Statistics in Medicine. (In Press)

Record type: Article

Abstract

Multi-arm 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 multi-arm 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 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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Submitted date: 25 March 2017
Accepted/In Press date: 28 February 2019
Keywords: missing Data, optimal design, dropout, longitudinal data
Organisations: Statistics

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Local EPrints ID: 407748
URI: https://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: 13 Mar 2019 20:07

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