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


Text available via DOI:

View graph of relations

Optimal design for experiments with possibly incomplete observations

Research output: Contribution to journalJournal articlepeer-review

<mark>Journal publication date</mark>2017
<mark>Journal</mark>Statistica Sinica
Number of pages26
Publication StatusPublished
Early online date20/04/17
<mark>Original language</mark>English


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.