The University of Southampton
University of Southampton Institutional Repository

Optimal design for experiments with possibly incomplete observations

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
1017-0405
1611-1632
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
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), 1611-1632. (doi:10.5705/ss.202015.0225).

Record type: Article

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.

Text
paperJun30.pdf - Author's Original
Download (380kB)
Text
2nd_revision_final - Accepted Manuscript
Download (368kB)

More information

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

Identifiers

Local EPrints ID: 378559
URI: http://eprints.soton.ac.uk/id/eprint/378559
ISSN: 1017-0405
PURE UUID: abaa3114-b4e6-46ad-ac3d-784012fe3a8a
ORCID for Stefanie Biedermann: ORCID iD orcid.org/0000-0001-8900-8268

Catalogue record

Date deposited: 08 Jul 2015 15:55
Last modified: 10 Jan 2022 02:51

Export record

Altmetrics

Contributors

Author: Kim May Lee
Author: Robin Mitra

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×