Sequential Monte Carlo for Bayesian sequentially designed experiments for discrete data

Christopher C. Drovandi, James M. McGree and Anthony N. Pettitt

Computational Statistics & Data Analysis, 2013, vol. 57, issue 1, 320-335

Abstract: In this paper we present a sequential Monte Carlo algorithm for Bayesian sequential experimental design applied to generalised non-linear models for discrete data. The approach is computationally convenient in that the information of newly observed data can be incorporated through a simple re-weighting step. We also consider a flexible parametric model for the stimulus–response relationship together with a newly developed hybrid design utility that can produce more robust estimates of the target stimulus in the presence of substantial model and parameter uncertainty. The algorithm is applied to hypothetical clinical trial or bioassay scenarios. In the discussion, potential generalisations of the algorithm are suggested to possibly extend its applicability to a wide variety of scenarios.

Keywords: Clinical trials; Generalised linear model; Generalised non-linear model; Sequential design; Sequential Monte Carlo; Target stimulus (search for similar items in EconPapers)
Date: 2013
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Citations: View citations in EconPapers (6)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:57:y:2013:i:1:p:320-335

DOI: 10.1016/j.csda.2012.05.014

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