Constructing Relative Effect Priors for Research Prioritization and Trial Design: A Meta-epidemiological Analysis

David Glynn, Georgios Nikolaidis, Dina Jankovic and Nicky J. Welton
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David Glynn: Centre for Health Economics, University of York, UK
Georgios Nikolaidis: IQVIA, London, UK
Dina Jankovic: Centre for Health Economics, University of York, UK
Nicky J. Welton: Bristol Medical School (PHS), University of Bristol, UK

Medical Decision Making, 2023, vol. 43, issue 5, 553-563

Abstract: Background Bayesian 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. Methods We 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. Results For 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. Conclusions This 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. Highlights Bayesian methods have potential to aid the efficient design of randomized clinical trials (RCTs) by incorporating existing evidence. Value-of-information (VOI) methods can be used to aid research prioritization by calculating the value of current decision uncertainty. These methods require a distribution describing current uncertainty in key parameters, that is, “prior distributions.†This article demonstrates a methodology to estimate prior distributions for relative treatment effects (odds and hazard ratios) estimated from a collection of previous RCTs. These results may be combined with expert elicitation to facilitate 1) value-of-information methods to prioritize research or 2) Bayesian methods for research design.

Keywords: meta-epidemiology; bayesian methods; value of information; research prioritisation; hierarchical models (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:sae:medema:v:43:y:2023:i:5:p:553-563

DOI: 10.1177/0272989X231165985

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