title: Robust meta-analytic-predictive priors in clinical trials with historical control information.
abstract: Historical information is always relevant for clinical trial design. Additionally, if incorporated in the analysis of a new trial, historical data allow to reduce the number of subjects. This decreases costs and trial duration, facilitates recruitment, and may be more ethical. Yet, under prior-data conflict, a too optimistic use of historical data may be inappropriate. We address this challenge by deriving a Bayesian meta-analytic-predictive prior from historical data, which is then combined with the new data. This prospective approach is equivalent to a meta-analytic-combined analysis of historical and new data if parameters are exchangeable across trials. The prospective Bayesian version requires a good approximation of the meta-analytic-predictive prior, which is not available analytically. We propose two- or three-component mixtures of standard priors, which allow for good approximations and, for the one-parameter exponential family, straightforward posterior calculations. Moreover, since one of the mixture components is usually vague, mixture priors will often be heavy-tailed and therefore robust. Further robustness and a more rapid reaction to prior-data conflicts can be achieved by adding an extra weakly-informative mixture component. Use of historical prior information is particularly attractive for adaptive trials, as the randomization ratio can then be changed in case of prior-data conflict. Both frequentist operating characteristics and posterior summaries for various data scenarios show that these designs have desirable properties. We illustrate the methodology for a phase II proof-of-concept trial with historical controls from four studies. Robust meta-analytic-predictive priors alleviate prior-data conflicts ' they should encourage better and more frequent use of historical data in clinical trials.
authors.affiliation: Statistical Methodology, Development, Novartis Pharma AG, Basel, Switzerland.
authors.forename: Heinz
authors.initials: H
authors.lastname: Schmidli
authors.forename: Sandro
authors.initials: S
authors.lastname: Gsteiger
authors.forename: Satrajit
authors.initials: S
authors.lastname: Roychoudhury
authors.forename: Anthony
authors.initials: A
authors.lastname: O'Hagan
authors.forename: David
authors.initials: D
authors.lastname: Spiegelhalter
authors.forename: Beat
authors.initials: B
authors.lastname: Neuenschwander
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D017322:Clinical Trials, Phase II as Topic; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D006801:Humans; D015201:Meta-Analysis as Topic; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods; D011379:Prognosis; D016032:Randomized Controlled Trials as Topic; D018401:Sample Size
keywords: Adaptive design; Adaptive randomization; Bayesian inference; Clinical trials; Exponential family; Meta-analysis; Mixture distribution; Robustness