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A particle filter for Bayesian sequential design

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Design and Analysis of Experiments

A particle filter approach is presented for sequential design with a focus on Bayesian adaptive dose finding studies for the estimation of the maximum tolerable dose. The approach is computationally convenient in that the information of newly observed data can be incorporated through a simple re-weighting step. Furthermore, the method does not require prior information represented as imagined data as in other dose finding approaches, although such data can be included straightforwardly if available. We also consider a flexible parametric model together with a newly developed hybrid design utility that can produce more robust estimates of the target dose in the presence of substantial model and parameter uncertainty.

This talk is part of the Isaac Newton Institute Seminar Series series.

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