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

Hill-McManus, Daniel and Hughes, Dyfrig A ORCID: 0000-0001-8247-7459
(2021) Combining Model-Based Clinical Trial Simulation, Pharmacoeconomics, and Value of Information to Optimize Trial Design. CPT-PHARMACOMETRICS & SYSTEMS PHARMACOLOGY, 10 (1). pp. 75-83.

Access the full-text of this item by clicking on the Open Access link.


The Bayesian decision-analytic approach to trial design uses prior distributions for treatment effects, updated with likelihoods for proposed trial data. Prior distributions for treatment effects based on previous trial results risks sample selection bias and difficulties when a proposed trial differs in terms of patient characteristics, medication adherence, or treatment doses and regimens. The aim of this study was to demonstrate the utility of using pharmacometric-based clinical trial simulation (CTS) to generate prior distributions for use in Bayesian decision-theoretic trial design. The methods consisted of four principal stages: a CTS to predict the distribution of treatment response for a range of trial designs; Bayesian updating for a proposed sample size; a pharmacoeconomic model to represent the perspective of a reimbursement authority in which price is contingent on trial outcome; and a model of the pharmaceutical company return on investment linking drug prices to sales revenue. We used a case study of febuxostat versus allopurinol for the treatment of hyperuricemia in patients with gout. Trial design scenarios studied included alternative treatment doses, inclusion criteria, input uncertainty, and sample size. Optimal trial sample sizes varied depending on the uncertainty of model inputs, trial inclusion criteria, and treatment doses. This interdisciplinary framework for trial design and sample size calculation may have value in supporting decisions during later phases of drug development and in identifying costly sources of uncertainty, and thus inform future research and development strategies.

Item Type: Article
Uncontrolled Keywords: Humans, Gout, Hyperuricemia, Allopurinol, Uric Acid, Models, Economic, Bayes Theorem, Uncertainty, Sample Size, Decision Theory, Models, Biological, Research Design, Computer Simulation, Economics, Pharmaceutical, Reimbursement Mechanisms, Investments, Clinical Trials, Phase III as Topic, Febuxostat, Drug Development
Divisions: Faculty of Health and Life Sciences
Faculty of Health and Life Sciences > Institute of Systems, Molecular and Integrative Biology
Depositing User: Symplectic Admin
Date Deposited: 09 Jul 2021 08:41
Last Modified: 18 Jan 2023 21:36
DOI: 10.1002/psp4.12579
Open Access URL:
Related URLs: