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Analytic approaches for research priority-setting: issues, challenges and the way forward

Published on Sep 3, 2015in Expert Review of Pharmacoeconomics & Outcomes Research1.828
· DOI :10.1586/14737167.2015.1087317
Lazaros Andronis11
Estimated H-index: 11
(University of Birmingham)
Abstract
In the last two decades, growing demand for studies assessing healthcare interventions, coupled with the acknowledgment that limited public funds for research ought to be allocated efficiently, has led to increasing calls for the use of analytic approaches for research prioritization. Two main approaches have been proposed - 'value of information' and 'prospective payback of research' - but neither of them is used formally in the prioritization process. This article discusses possible barriers to the formal adoption and use of analytic approaches. These include uncertainties around the appropriateness of using results of analytic approaches for priority-setting, questions around the validity of the results and challenges in introducing and using analytical methods as part of the prioritization process. It is argued that most of these challenges can be overcome and ways of doing so are discussed. The author's view on the direction and pace of future developments is provided, followed by recommendations for further research.
  • References (70)
  • Citations (4)
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References70
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#1Hawre Jalal (VA Palo Alto Healthcare System)H-Index: 9
#2Jeremy D. Goldhaber-Fiebert (Stanford University)H-Index: 32
Last. Karen M. KuntzH-Index: 69
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Decision makers often desire both guidance on the most cost-effective interventions given current knowledge and also the value of collecting additional information to improve the decisions made (i.e., from value of information [VOI] analysis). Unfortunately, VOI analysis remains underused due to the conceptual, mathematical, and computational challenges of implementing Bayesian decision-theoretic approaches in models of sufficient complexity for real-world decision making. In this study, we prop...
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#1Mark Strong (University of Sheffield)H-Index: 21
#2Jeremy E. Oakley (University of Sheffield)H-Index: 27
Last. Penny Breeze (University of Sheffield)H-Index: 7
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Health economic decision-analytic models are used to estimate the expected net benefits of competing decision options. The true values of the input parameters of such models are rarely known with certainty, and it is often useful to quantify the value to the decision maker of reducing uncertainty through collecting new data. In the context of a particular decision problem, the value of a proposed research design can be quantified by its expected value of sample information (EVSI). EVSI is common...
37 CitationsSource
#1Nicky J Welton (UoB: University of Bristol)H-Index: 48
#2Howard Thom (UoB: University of Bristol)H-Index: 10
Expected value of sample information (EVSI) 1 measures the average net-benefit gain from conducting new research and can be used to inform decisions on which new studies to fund and how best to design those studies. This helps avoid wasting resources researching treatments that were never likely to be cost-effective or conversely by adopting treatments that, if more evidence were collected, may be shown not to be cost-effective. However, the calculations in the general case rely on nested simula...
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#1Matthew Glover (Brunel University London)H-Index: 15
#2Martin Buxton (Brunel University London)H-Index: 54
Last. Jonathan Grant ('KCL': King's College London)H-Index: 26
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© 2014 Glover et al.; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise...
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#1Jason Madan (UoB: University of Bristol)H-Index: 20
#2A E Ades (UoB: University of Bristol)H-Index: 54
Last. Nicky J Welton (UoB: University of Bristol)H-Index: 48
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Expected value of information methods evaluate the potential health benefits that can be obtained from conducting new research to reduce uncertainty in the parameters of a cost-effectiveness analysis model, hence reducing decision uncertainty. Expected value of partial perfect information (EVPPI) provides an upper limit to the health gains that can be obtained from conducting a new study on a subset of parameters in the cost-effectiveness analysis and can therefore be used as a sensitivity analy...
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#1Mark Strong (University of Sheffield)H-Index: 21
#2Jeremy E. Oakley (University of Sheffield)H-Index: 27
The value of learning an uncertain input in a decision model can be quantified by its partial expected value of perfect information (EVPI). This is commonly estimated via a 2-level nested Monte Carlo procedure in which the parameter of interest is sampled in an outer loop, and then conditional on this sampled value, the remaining parameters are sampled in an inner loop. This 2-level method can be difficult to implement if the joint distribution of the inner-loop parameters conditional on the par...
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It has already been pointed out that the bootstrap can be used to calculate the expected value of perfect information (EVPI) when individual-level data from a randomized controlled trial (RCT) is at hand. However, as mentioned by others, it is not clear if and how such a method can be extended to calculate the expected value of sample information (EVSI). In this article, we provide a nonparametric definition for EVPI and EVSI, which is based on considering the entire population distribution as t...
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#1Stephen Hanney (Brunel University London)H-Index: 23
#2Amanda Watt (AN: RAND Corporation)H-Index: 3
Last. Leanne MetcalfH-Index: 1
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Background Debate is intensifying about how to assess the full range of impacts from medical research. Complexity increases when assessing the diverse funding streams of funders such as Asthma UK, a charitable patient organisation supporting medical research to benefit people with asthma. This paper aims to describe the various impacts identified from a range of Asthma UK research, and explore how Asthma UK utilised the characteristics of successful funding approaches to inform future research s...
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Abstract Background The expected value of partial perfect information (EVPPI) is a theoretically justifiable and informative measure of uncertainty in decision-analytic cost-effectiveness models, but its calculation is computationally intensive because it generally requires two-level Monte Carlo simulation. We introduce an efficient, one-level simulation method for the calculation of single-parameter EVPPI. Objective We show that under mild regularity assumptions, the expectation-maximization-ex...
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Objective This article provides a systematic and critical review of the evolving methods and applications of value of information (VOI) in academia and practice and discusses where future research needs to be directed. Methods Published VOI studies were identified by conducting a computerized search on Scopus and ISI Web of Science from 1980 until December 2011 using pre-specified search terms. Only full-text papers that outlined and discussed VOI methods for medical decision making, and studies...
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When making funding decisions, research organisations largely consider the merits (e.g. scientific rigour and feasibility) of submitted research proposals; yet, there is often little or no reference to their value for money. This may be attributed to the challenges of assessing and integrating value of research into existing research prioritisation processes. We propose a framework that considers both the merits of research and its value for money to guide health research funding decisions. A pr...
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Background. Although value of information (VOI) analyses are increasingly advocated and used for research prioritization and reimbursement decisions, the interpretation and usefulness of VOI outcom...
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