Abstract

Many experiments in all areas of science, technology and industry measure a response that cannot be adequately described by a linear model with normally distributed errors. In addition, the further complication often arises of needing to arrange the experiment into blocks of homogeneous units. Examples include industrial manufacturing experiments with binary responses, clinical trials where subjects receive multiple treatments and crystallography experiments in early-stage drug discovery. This talk will present some new approaches to the design of such experiments, assuming both conditional (subject-specific) and marginal (population-averaged) models. The different methods will be compared, and some advantages and disadvantages highlighted. Common issues, including the impact of correlations and the dependence of the design on the values of model parameters, will also be discussed.