Literature DB >> 19340851

The Rheumatoid Arthritis Drug Development Model: a case study in Bayesian clinical trial simulation.

Richard M Nixon1, Anthony O'Hagan, Jeremy Oakley, Jason Madan, John W Stevens, Nick Bansback, Alan Brennan.   

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 (c) 2009 John Wiley & Sons, Ltd.

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Year:  2009        PMID: 19340851     DOI: 10.1002/pst.368

Source DB:  PubMed          Journal:  Pharm Stat        ISSN: 1539-1604            Impact factor:   1.894


  6 in total

Review 1.  Quantifying factors for the success of stratified medicine.

Authors:  Mark R Trusheim; Breon Burgess; Sean Xinghua Hu; Theresa Long; Steven D Averbuch; Aiden A Flynn; Alfons Lieftucht; Abhijit Mazumder; Judy Milloy; Peter M Shaw; David Swank; Jian Wang; Ernst R Berndt; Federico Goodsaid; Michael C Palmer
Journal:  Nat Rev Drug Discov       Date:  2011-10-31       Impact factor: 84.694

Review 2.  Integration of PKPD relationships into benefit-risk analysis.

Authors:  Francesco Bellanti; Rob C van Wijk; Meindert Danhof; Oscar Della Pasqua
Journal:  Br J Clin Pharmacol       Date:  2015-07-29       Impact factor: 4.335

3.  A Quantitative Process for Enhancing End of Phase 2 Decisions.

Authors:  Tony Sabin; James Matcham; Sarah Bray; Andrew Copas; Mahesh K B Parmar
Journal:  Stat Biopharm Res       Date:  2014-02-01       Impact factor: 1.452

Review 4.  Decision-theoretic designs for small trials and pilot studies: A review.

Authors:  Siew Wan Hee; Thomas Hamborg; Simon Day; Jason Madan; Frank Miller; Martin Posch; Sarah Zohar; Nigel Stallard
Journal:  Stat Methods Med Res       Date:  2015-06-05       Impact factor: 3.021

5.  Combining Model-Based Clinical Trial Simulation, Pharmacoeconomics, and Value of Information to Optimize Trial Design.

Authors:  Daniel Hill-McManus; Dyfrig A Hughes
Journal:  CPT Pharmacometrics Syst Pharmacol       Date:  2020-12-31

6.  Models of excellence: improving oncology drug development.

Authors:  M R Sharma; M L Maitland; M J Ratain
Journal:  Clin Pharmacol Ther       Date:  2012-11       Impact factor: 6.875

  6 in total

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