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Bayesian predictive power: choice of prior and some recommendations for its use as probability of success in drug development
Author(s) -
Rufibach Kaspar,
Burger Hans Ulrich,
Abt Markus
Publication year - 2016
Publication title -
pharmaceutical statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.421
H-Index - 38
eISSN - 1539-1612
pISSN - 1539-1604
DOI - 10.1002/pst.1764
Subject(s) - interpretability , prior probability , bayesian probability , predictive power , statistical power , bayesian statistics , probability density function , econometrics , bayesian linear regression , statistics , computer science , bayes' theorem , mathematics , machine learning , bayesian inference , philosophy , epistemology
Bayesian predictive power, the expectation of the power function with respect to a prior distribution for the true underlying effect size, is routinely used in drug development to quantify the probability of success of a clinical trial. Choosing the prior is crucial for the properties and interpretability of Bayesian predictive power. We review recommendations on the choice of prior for Bayesian predictive power and explore its features as a function of the prior. The density of power values induced by a given prior is derived analytically and its shape characterized. We find that for a typical clinical trial scenario, this density has a u ‐shape very similar, but not equal, to a β ‐distribution. Alternative priors are discussed, and practical recommendations to assess the sensitivity of Bayesian predictive power to its input parameters are provided. Copyright © 2016 John Wiley & Sons, Ltd.