We consider the problem of experimental design when the response is modeled by a generalized linear model (GLM) and the experimental plan can be determined sequentially. Most prior research on this problem has been limited to the case of one-factor, binary response experiments, which are encountered in dose-response studies and sensitivity testing. We suggest a new procedure for the sequential choice of observations, that improves on existing methods in four important ways:
(1) It can be applied to multi-factor experiments and is not limited to the one-factor setting;
(2) It can be used with any GLM, not just binary responses;
(3) Both fully sequential and group sequential settings are treated; and
(4) The experimenter is not constrained to specify a single model and can use the prior to reflect uncertainty as to the link function and the form of the linear predictor.
Our procedure is based on a D-optimality criterion, and on a Bayesian analysis that exploits a discretization of the parameter space to efficiently represent the posterior distribution. In the one-factor setting, a simulation study shows that our method is superior in efficiency to commonly used procedures, such as the"Bruceton" test (Dixon and Mood, 1948), the Langlie (1965) test or Neyer's (1994) procedure. We also present a comparison of results obtained with the new algorithm versus the "Bruceton" method on an actual sensitivity test conducted recently at an industrial plant.
Hovav A. Dror and David M. Steinberg (2008). Sequential Experimental Designs for Generalized Linear Models, Journal of the American Statistical Association, 103, 288-298.
Source Code for algorithms and Examples
Source code is written on MATLAB and requires the Statistical Toolbox. Most of the files are given in two versions: “regular” and “auto”. When the name of the file includes “auto”, the file demonstrates the algorithm by running automatically, with observations randomized in accordance to a chosen “true” reality. The “regular” files require the user to either accept or change the algorithm’s recommendation for the location of the next observation(s), and then to enter the outcome of the observation.
Sensitivity Test. This source code is the choice for “dose-response” experiments and “sensitivity tests”, and utilizes the example from section 4.1 of the paper. Files: SensitivityTest.m, SensitivityTestAUTO.m, Screenshot: SensitivityTestScreenshot.jpg
Bayesian analysis is utilized within all these files, but can also be found here
Back to the parent page: Experimental Design for GLM