Home > Research > Publications & Outputs > Optimal design for experiments with possibly in...

Electronic data

  • SS-2015-0225_na

    Accepted author manuscript, 1.53 MB, PDF document

    Available under license: CC BY-NC: Creative Commons Attribution-NonCommercial 4.0 International License

Links

Text available via DOI:

View graph of relations

Optimal design for experiments with possibly incomplete observations

Research output: Contribution to Journal/MagazineJournal articlepeer-review

Published

Standard

Optimal design for experiments with possibly incomplete observations. / Lee, Kim May; Biedermann, Stefanie; Mitra, Robin.
In: Statistica Sinica, 2017.

Research output: Contribution to Journal/MagazineJournal articlepeer-review

Harvard

APA

Vancouver

Lee KM, Biedermann S, Mitra R. Optimal design for experiments with possibly incomplete observations. Statistica Sinica. 2017. Epub 2017 Apr 20. doi: 10.5705/ss.202015.0225

Author

Lee, Kim May ; Biedermann, Stefanie ; Mitra, Robin. / Optimal design for experiments with possibly incomplete observations. In: Statistica Sinica. 2017.

Bibtex

@article{c367be16a89c4b28a6897d3b11c229a0,
title = "Optimal design for experiments with possibly incomplete observations",
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.",
keywords = "covariance matrix, information matrix, linear regressionmodel, missing observations, optimal design",
author = "Lee, {Kim May} and Stefanie Biedermann and Robin Mitra",
year = "2017",
doi = "10.5705/ss.202015.0225",
language = "English",
journal = "Statistica Sinica",
issn = "1017-0405",
publisher = "Institute of Statistical Science",

}

RIS

TY - JOUR

T1 - Optimal design for experiments with possibly incomplete observations

AU - Lee, Kim May

AU - Biedermann, Stefanie

AU - Mitra, Robin

PY - 2017

Y1 - 2017

N2 - 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.

AB - 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.

KW - covariance matrix

KW - information matrix

KW - linear regressionmodel

KW - missing observations

KW - optimal design

U2 - 10.5705/ss.202015.0225

DO - 10.5705/ss.202015.0225

M3 - Journal article

JO - Statistica Sinica

JF - Statistica Sinica

SN - 1017-0405

ER -