University of Sheffield

Tony O'Hagan - Academic pages - Abstracts


Robust Meta-Analytic-Predictive Priors in Clinical Trials with Historical Control Information

Heinz Schmidli, Sandro Gsteiger, Satrajit Roychoudhury, Anthony O'Hagan, David Spiegelhalter and Beat Neuenschwander

Statistical Methodology, Development, Novartis Pharma AG, Switzerland; Institute of Social and Preventive Medicine, University of Bern, Switzerland,; Statistical Methodology, Oncology, Novartis Pharmaceuticals Corporation, U.S.A.; School of Mathematics and Statistics, University of Sheffield, UK; Statistical Laboratory, University of Cambridge, U.K; Statistical Methodology, Oncology, Novartis Pharma AG, Switzerland.

Publication details: Biometrics 70, 1023-1032, 2014.


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.

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Updated: 6 January 2015
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