title: Bayesian predictive power: choice of prior and some recommendations for its use as probability of success in drug development.
abstract: 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.
authors.affiliation: Department of Biostatistics, Hoffmann-La Roche Ltd, Basel, Switzerland. kaspar.rufibach@roche.com.
authors.forename: Kaspar
authors.initials: K
authors.lastname: Rufibach
authors.affiliation: Department of Biostatistics, Hoffmann-La Roche Ltd, Basel, Switzerland.
authors.forename: Hans Ulrich
authors.initials: HU
authors.lastname: Burger
authors.affiliation: Department of Biostatistics, Hoffmann-La Roche Ltd, Basel, Switzerland.
authors.forename: Markus
authors.initials: M
authors.lastname: Abt
mesh_terms: D001499:Bayes Theorem; D002755:Choice Behavior; D055808:Drug Discovery / Q000379:methods / Q000706:statistics & numerical data*; D005544:Forecasting; D006801:Humans; D011336:Probability
keywords: Bayesian predictive power; conditional power; prior distribution; probability of technical success