JSM 2005 - Toronto

Abstract #304166

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Legend: = Applied Session, = Theme Session, = Presenter
Activity Number: 361
Type: Contributed
Date/Time: Wednesday, August 10, 2005 : 8:30 AM to 10:20 AM
Sponsor: Biopharmaceutical Section
Abstract - #304166
Title: Optimal Experimental Designs for Drug Synergism Studies
Author(s): Donald White*+ and William R. Greco
Companies: The University of Toledo and Roswell Park Cancer Institute
Address: Department of Mathematics, Toledo, OH, 43606-3390, United States
Keywords: Hill model ; D-optimality ; mixture experiments ; nonlinear regression
Abstract:

We study optimal experimental designs for performing combination studies. Due to the combination studies automatically increasing exponentially in size with the number of agents and observations being costly, efforts at minimizing numbers of observations by optimizing designs are crucial. Using the mathematical programming language Matlab, we determine D-optimal and other designs for two and three drug models with two and more parameters. We use a descent algorithm to provide an initial approximation to the optimal design, then search for the optimal design. Finally, we confirm the design points using results from the General Equivalence Theorem. We consider hierarchical models where the Hill model is the base model and the Hill parameters are polynomial functions of the drug fractions. We also use Bayesian methods to obtain designs that take into account uncertainty in our knowledge of the appropriate model structure, details, and/or parameters. Simulations are used to determine design efficiency and compare efficiencies of derived designs to those of standard ray designs. Supported by NIH RR10742 and CA16056.

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Revised March 2005