Abstract
BackgroundBayesian methods have potential for efficient design of randomized clinical trials (RCTs) by incorporating existing evidence. Furthermore, value of information (VOI) methods estimate the value of reducing decision uncertainty, aiding transparent research prioritization. These methods require a prior distribution describing current uncertainty in key parameters, such as relative treatment effect (RTE). However, at the time of designing and commissioning research, there may be no data to base the prior on. The aim of this article is to present methods to construct priors for RTEs based on a collection of previous RCTs.MethodsWe developed 2 Bayesian hierarchical models that captured variability in RTE between studies within disease area accounting for study characteristics. We illustrate the methods using a data set of 743 published RCTs across 9 disease areas to obtain predictive distributions for RTEs for a range of disease areas. We illustrate how the priors from such an analysis can be used in a VOI analysis for an RCT in bladder cancer and compare the results with those using an uninformative prior.ResultsFor most disease areas, the predicted RTE favored new interventions over comparators. The predicted effects and uncertainty differed across the 9 disease areas. VOI analysis showed that the expected value of research is much lower with our empirically derived prior compared with an uninformative prior.ConclusionsThis study demonstrates a novel approach to generating informative priors that can be used to aid research prioritization and trial design. The methods can also be used to combine RCT evidence with expert opinion. Further work is needed to create a rich database of RCT evidence that can be used to form off-the-shelf priors.
| Original language | English |
|---|---|
| Pages (from-to) | 553 - 563 |
| Number of pages | 11 |
| Journal | Medical Decision Making |
| Volume | 43 |
| Issue number | 5 |
| Early online date | 14 Apr 2023 |
| DOIs | |
| Publication status | Published - 5 Jul 2023 |
Bibliographical note
Funding Information:This work was primarily carried out at the Centre for Health Economics, University of York, UK, and Bristol Medical School (PHS), University of Bristol, UK. The work was presented at the CHE Seminar at University of York 3 September 2020, the meta-analysis in medicine meeting (MiM) 8 December 2020, and University of Bristol 23 February 2021. The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: Financial support for DG, GN, and DJ was provided in part by a contract with the National Institute for Health Research (NIHR) Evaluation, Trials and Studies Coordinating Centre (NETSCC). Financial support for NJW in this study was provided entirely by a contract with the NIHR Biomedical Research Centre at University Hospitals Bristol and Weston NHS Foundation Trust and the University of Bristol. The funding agreement ensured the authors’ independence in designing the study, interpreting the data, writing, and publishing the report. The views expressed are those of the authors and not necessarily those of the NIHR or the Department of Health and Social Care.
Publisher Copyright:
© The Author(s) 2023.
Research Groups and Themes
- HEHP@Bristol
- Multi-parameter Evidence Synthesis Research
