Optimal design for experiments with possibly incomplete observations
Optimal design for experiments with possibly incomplete observations
Missing responses occur in many industrial or medical experiments, for example in clinical trials where slow acting treatments are assessed. Finding efficient designs for such experiments can be problematic since it is not known at the design stage which observations will be missing. The design literature mainly focuses on assessing robustness of designs for missing data scenarios, rather than finding designs which are optimal in this situation. Imhof, Song and Wong (2002) propose a framework for design search, based on the expected information matrix. We develop a new approach which includes Imhof, Song and Wong (2002)'s method as special case and justifies its use retrospectively. Our method is illustrated through a simulation study based on real data from an Alzheimer's disease trial.
covariance matrix, information matrix, linear regressionmodel, missing observations, optimal design
1611-1632
Lee, Kim May
8111b847-1f07-460f-88d7-e5a24d798e3a
Biedermann, Stefanie
fe3027d2-13c3-4d9a-bfef-bcc7c6415039
Mitra, Robin
2b944cd7-5be8-4dd1-ab44-f8ada9a33405
31 July 2018
Lee, Kim May
8111b847-1f07-460f-88d7-e5a24d798e3a
Biedermann, Stefanie
fe3027d2-13c3-4d9a-bfef-bcc7c6415039
Mitra, Robin
2b944cd7-5be8-4dd1-ab44-f8ada9a33405
Lee, Kim May, Biedermann, Stefanie and Mitra, Robin
(2018)
Optimal design for experiments with possibly incomplete observations.
Statistica Sinica, 28 (3), .
(doi:10.5705/ss.202015.0225).
Abstract
Missing responses occur in many industrial or medical experiments, for example in clinical trials where slow acting treatments are assessed. Finding efficient designs for such experiments can be problematic since it is not known at the design stage which observations will be missing. The design literature mainly focuses on assessing robustness of designs for missing data scenarios, rather than finding designs which are optimal in this situation. Imhof, Song and Wong (2002) propose a framework for design search, based on the expected information matrix. We develop a new approach which includes Imhof, Song and Wong (2002)'s method as special case and justifies its use retrospectively. Our method is illustrated through a simulation study based on real data from an Alzheimer's disease trial.
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Submitted date: 2015
Accepted/In Press date: 19 April 2017
e-pub ahead of print date: 31 July 2018
Published date: 31 July 2018
Keywords:
covariance matrix, information matrix, linear regressionmodel, missing observations, optimal design
Organisations:
Statistics
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Local EPrints ID: 378559
URI: http://eprints.soton.ac.uk/id/eprint/378559
ISSN: 1017-0405
PURE UUID: abaa3114-b4e6-46ad-ac3d-784012fe3a8a
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Date deposited: 08 Jul 2015 15:55
Last modified: 15 Mar 2024 05:19
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Author:
Kim May Lee
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