White Rose University Consortium logo
University of Leeds logo University of Sheffield logo York University logo

The rheumatoid arthritis drug development model: a case study in Bayesian clinical trial simulation

Nixon, R.M., O'Hagan, A., Oakley, J.E., Madan, J., Stevens , J.W., Bansback, N. and Brennan, A. (2009) The rheumatoid arthritis drug development model: a case study in Bayesian clinical trial simulation. Pharmaceutical Statistics, 8 (4). pp. 371-389. ISSN 1539-1604

Full text not available from this repository. (Request a copy)

Abstract

The development of a new drug is a major undertaking and it is important to consider carefully the key decisions in the development process. Decisions are made in the presence of uncertainty and outcomes such as the probability of successful drug registration depend on the clinical development programmme. The Rheumatoid Arthritis Drug Development Model was developed to support key decisions for drugs in development for the treatment of rheumatoid arthritis. It is configured to simulate Phase 2b and 3 trials based on the efficacy of new drugs at the end of Phase 2a, evidence about the efficacy of existing treatments, and expert opinion regarding key safety criteria. The model evaluates the performance of different development programmes with respect to the duration of disease of the target population, Phase 2b and 3 sample sizes, the dose(s) of the experimental treatment, the choice of comparator, the duration of the Phase 2b clinical trial, the primary efficacy outcome and decision criteria for successfully passing Phases 2b and 3. It uses Bayesian clinical trial simulation to calculate the probability of successful drug registration based on the uncertainty about parameters of interest, thereby providing a more realistic assessment of the likely outcomes of individual trials and sequences of trials for the purpose of decision making. In this case study, the results show that, depending on the trial design, the new treatment has assurances of successful drug registration in the range 0.044-0.142 for an ACR20 outcome and 0.057-0.213 for an ACR50 outcome. Copyright © 2009 John Wiley & Sons, Ltd.

Item Type: Article
Keywords: Bayesian; simulation; assurance; drug development
Institution: The University of Sheffield
Academic Units: The University of Sheffield > Faculty of Science (Sheffield) > School of Mathematics and Statistics (Sheffield)
Depositing User: Mrs Megan Hobbs
Date Deposited: 29 Mar 2010 11:28
Last Modified: 07 Jun 2010 11:09
Published Version: http://dx.doi.org/10.1002/pst.368
Status: Published
Publisher: John Wiley & Sons
Identification Number: 10.1002/pst.368
URI: http://eprints.whiterose.ac.uk/id/eprint/10620

Actions (repository staff only: login required)