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The importance of adjusting for potential confounders in Bayesian hierarchical models synthesising evidence from randomised and non-randomised studies: an application comparing treatments for abdominal aortic aneurysms

Published on Dec 1, 2010in BMC Medical Research Methodology2.509
· DOI :10.1186/1471-2288-10-64
C. Elizabeth McCarron5
Estimated H-index: 5
(St. Joseph's Healthcare Hamilton),
Eleanor Pullenayegum19
Estimated H-index: 19
(St. Joseph's Healthcare Hamilton)
+ 2 AuthorsJean-Eric Tarride29
Estimated H-index: 29
(St. Joseph's Healthcare Hamilton)
Abstract
Background Informing health care decision making may necessitate the synthesis of evidence from different study designs (e.g., randomised controlled trials, non-randomised/observational studies). Methods for synthesising different types of studies have been proposed, but their routine use requires development of approaches to adjust for potential biases, especially among non-randomised studies. The objective of this study was to extend a published Bayesian hierarchical model to adjust for bias due to confounding in synthesising evidence from studies with different designs.
  • References (19)
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#1Alex J. Sutton (University of Leicester)H-Index: 86
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The randomised controlled trial (RCT) has developed a central role in applied cost-effectiveness studies in health care as the vehicle for analysis. This paper considers the role of trial-based economic evaluation in this era of explicit decision making. It is argued that any framework for economic analysis can only be judged insofar as it can inform two key decisions and be consistent with the objectives of a health care system subject to its resource constraints. The two decisions are, firstly...
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