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Title: Bayesian meta-analysis on medical devices: Application to implantable cardioverter defibrillators
Authors: Youn, J-H
Lord, J
Hemming, K
Girling, A
Buxton, M
Keywords: Bayesian analysis;Meta-analysis;Medical devices;Expert opinions;Defibrillator;Implantable
Issue Date: 2012
Publisher: Cambridge University Press
Citation: International Journal of Technology Assessment in Health Care, 28(2),115–124, 2012 .
Abstract: Objectives: The aim of this study is to describe and illustrate a method to obtain early estimates of the effectiveness of a new version of a medical device. Methods: In the absence of empirical data, expert opinion may be elicited on the expected difference between the conventional and modified devices. Bayesian Mixed Treatment Comparison (MTC) meta-analysis can then be used to combine this expert opinion with existing trial data on earlier versions of the device. We illustrate this approach for a new four-pole implantable cardioverter defibrillator (ICD) compared with conventional ICDs, Class III anti-arrhythmic drugs, and conventional drug therapy for the prevention of sudden cardiac death in high risk patients. Existing RCTs were identified from a published systematic review, and we elicited opinion on the difference between four-pole and conventional ICDs from experts recruited at a cardiology conference. Results: Twelve randomized controlled trials were identified. Seven experts provided valid probability distributions for the new ICDs compared with current devices. The MTC model resulted in estimated relative risks of mortality of 0.74 (0.60–0.89) (predictive relative risk [RR] = 0.77 [0.41–1.26]) and 0.83 (0.70–0.97) (predictive RR = 0.84 [0.55–1.22])with the new ICD therapy compared to Class III anti-arrhythmic drug therapy and conventional drug therapy, respectively. These results showed negligible differences from the preliminary results for the existing ICDs. Conclusions: The proposed method incorporating expert opinion to adjust for a modification made to an existing device may play a useful role in assisting decision makers to make early informed judgments on the effectiveness of frequently modified healthcare technologies.
Description: The online version of this article is published within an Open Access environment subject to the conditions of the Creative Commons Attribution-NonCommercial-ShareAlike licence <>. The written permission of Cambridge University Press must be obtained for commercial re-use.
ISSN: 0266-4623
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Brunel OA Publishing Fund
Health Economics Research Group (HERG)

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