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)
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.
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