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Setting priorities for research: a practical application of ‘payback’ and expected value of information

✍ Scribed by Rachael L. Fleurence


Publisher
John Wiley and Sons
Year
2007
Tongue
English
Weight
155 KB
Volume
16
Category
Article
ISSN
1057-9230

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✦ Synopsis


Abstract

Background: Setting priorities for research using economic in addition to scientific criteria can ensure that resources are spent efficiently and equitably.

Objective: This study applies two priority setting methods ‘payback’ and expected value of information (EVI) to two research areas (osteoporosis and pressure ulcers) and where appropriate to four clinical trials: the Record Trial, the Vitamin D and Calcium Trial and the Hip Protector Trial (osteoporosis), and the Pressure Trial (wound care).

Methods: Two decision‐analytic models were developed. For ‘payback’, the PATHS model was used to estimate the expected net benefits of conducting the four clinical trials. An EVI framework was applied to estimate the cost‐effectiveness of conducting further research in the two disease areas investigated.

Results: The application of ‘payback’ suggests that the Record Trial and the Vitamin D and Calcium Trial would be cost‐effective. The Hip Protector and the Pressure Ulcer Trial are cost‐effective under certain assumptions concerning the likelihood of obtaining positive, negative or inconclusive results. The EVI method suggests that research would be potentially cost‐effective in these areas in the populations considered.

Conclusion: EVI provides strategic information for setting priorities for research between disease areas and study populations. ‘Payback’ provides information on the cost‐effectiveness of specific research designs. However, further work in this area, particularly concerning the issue of implementation of research, is required. Copyright © 2007 John Wiley & Sons, Ltd.


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## Abstract Expected value of sample information (EVSI) involves simulating data collection, Bayesian updating, and re‐examining decisions. Bayesian updating in Weibull models typically requires Markov chain Monte Carlo (MCMC). We examine five methods for calculating posterior expected net benefit