To suggest a procedure for building up a design prior based on available historical data, that is, results from previous similar experiments, in Bayesian sample size determination. The authors consider the sample size determination (SSD) problem, which is a basic yet extremely important aspect of experimental design. Specifically, they deal with the Bayesian approach to SSD, which gives researchers the possibility of taking into account preexperimental information and uncertainty on unknown parameters. In the design stage, this fact offers the advantage of removing or mitigating typical drawbacks of classical methods, which might lead to serious miscalculation of the sample size. In this context, the leading idea is to choose the minimal sample size that guarantees a probabilistic control on the performance of quantities that are derived from the posterior distribution and used for inference on parameters of interest. The authorsare concerned with the use of historical data, Le., observations from previous similar studies for SSD. They illustrate how the class of power priors can be truthfully employed to deal with lack of homogeneity between historical data and observations of the upcoming experiment.
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