Optimal Design for two-parameter nonlinear models with applications to survival models

Alan Kimber (University of Southampton)

Friday 8th March, 2013 15:00-16:00 Maths 204


Censoring occurs in many industrial or biomedical `time to event' experiments. Finding efficient designs for such experiments can be problematic since the statistical models involved will usually be nonlinear, making the optimal choice of design parameter dependent. We provide analytical characterisations of locally D- and c-optimal designs for a class of models, thus reducing the numerical effort for design search substantially. We also investigate standadised maximin D-and c-optimal designs. We illustrate our results using the natural proportional hazards parameterisation of the exponential regression model. Different censoring mechanisms are incorporated and the robustness of designs against parameter misspecification is assessed.

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