Optimal design for experiments with possibly incomplete observations

Lee, Kim May; Biedermann, Stefanie and Mitra, Robin (2018). Optimal design for experiments with possibly incomplete observations. Statistica Sinica, 28(3) pp. 1611–1632.

DOI: https://doi.org/10.5705/ss.202015.0225

Abstract

Missing responses occur in many industrial and medical experiments, for example in clinical trials where slow acting treatments are assessed. Finding efficient designs for such experiments is 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 an approach that 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 data from an Alzheimer’s disease trial.

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About

• Item ORO ID
• 72259
• Item Type
• Journal Item
• ISSN
• 1017-0405
• Keywords
• Covariance matrix; information matrix; linear regression model; missing observations; optimal design
• Academic Unit or School
• Faculty of Science, Technology, Engineering and Mathematics (STEM) > Mathematics and Statistics
  Faculty of Science, Technology, Engineering and Mathematics (STEM)
• Related URLs
• • https://eprints.soton.ac.uk/378559/(Other)
• Depositing User
• Stefanie Biedermann