
The expected value of the standard power function of a test, computed with respect to a design prior distribution, is often used to evaluate the probability of success of an experiment. However, looking only at the expected value might be reductive. Instead, the whole probability distribution of the power function induced by the design prior can be exploited. In this article we consider one‐sided testing for the scale parameter of exponential families and we derive general unifying expressions for cumulative distribution and density functions of the random power. Sample size determination criteria based on alternative summaries of these functions are discussed. The study sheds light on the relevance of the choice of the design prior in order to construct a successful experiment.
clinical trials, Bayesian power, probability of success, Bayes Theorem, Bayesian design, Applications of statistics to biology and medical sciences; meta analysis, sample size determination, Sample Size, Humans, Bayesian design; Bayesian power; clinical trials; probability of success; sample size determination, Probability
clinical trials, Bayesian power, probability of success, Bayes Theorem, Bayesian design, Applications of statistics to biology and medical sciences; meta analysis, sample size determination, Sample Size, Humans, Bayesian design; Bayesian power; clinical trials; probability of success; sample size determination, Probability
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