Health Research and Policy

Workshop in Biostatistics - Abstract

DATE: May 15, 2014
TIME: 1:15 - 3:00 pm
LOCATION: Medical School Office Building, Rm x303
TITLE: Using Animal Instincts to Find Efficient Experimental Designs
SPEAKER: Weng Kee Wong, PhD
Professor, Department of Biostatistics, Fielding School of Public Health,
University of California at Los Angeles

I first present a brief overview of optimal design methodology. Particle swarm optimization (PSO) is then introduced to find optimal designs for potentially any model and any design criterion. The method works quite magically and frequently finds the optimal design or a nearly optimal design in a relatively short time. There is virtually no explicit assumption required for the method to perform well and the user only needs to input a few easy tuning parameters in the PSO algorithm.

Using popular models from the biomedical sciences as examples, I demonstrate how PSO searches for different types of optimal designs in dose response studies, including mini-max types of optimal designs where effective algorithms to find such designs have remained elusive until now.

Suggested readings:
L.A. Khinkis, L. Levasseur, H. Faessel, and W.R. Greco (2003). Optimal Design for Estimating Parameters of the 4-Parameter Hill Model. Nonlinearity in Biology, Toxicology, and Medicine,, 1:, 363-377. DOI: 10.1080/15401420390249925

R.B. Chen, S.P. Chang, W. Wang, H.C. Tung and W.K. Wong. Optimal Minimax Designs via Particle Swarm Optimization Methods Stat Comput,, Received: 27 June 2013/Accepted: 24 March 2014 @ Springer Science+Business Media New York 2014. DOI: 10.1007/s11222-014-9466-0

R. Dennis Cook and Weng Kee Wong. On the Equivalence of Constrained and Compound Optimal Designs JASA, 89:, Issue 426, 1994.

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