Literature DB >> 2182960

An introduction to a Bayesian method for meta-analysis: The confidence profile method.

D M Eddy1, V Hasselblad, R Shachter.   

Abstract

The Confidence Profile Method is a new Bayesian method that can be used to assess technologies where the available evidence involves a variety of experimental designs, types of outcomes, and effect measures; a variety of biases; combinations of biases and nested bases; uncertainty about biases; an underlying variability in the parameter of interest; indirect evidence; and technology families. The result of an analysis with the Confidence Profile Method is a posterior distribution for the parameter of interest, posterior distributions for other parameters, and a covariance matrix for all the parameters in the model. The posterior distributions incorporate all the uncertainty the assessor chooses to describe about any of the parameters used in the analysis.

Mesh:

Year:  1990        PMID: 2182960     DOI: 10.1177/0272989X9001000104

Source DB:  PubMed          Journal:  Med Decis Making        ISSN: 0272-989X            Impact factor:   2.583


  12 in total

Review 1.  Methods in health service research. An introduction to bayesian methods in health technology assessment.

Authors:  D J Spiegelhalter; J P Myles; D R Jones; K R Abrams
Journal:  BMJ       Date:  1999-08-21

Review 2.  Handling uncertainty in cost-effectiveness models.

Authors:  A H Briggs
Journal:  Pharmacoeconomics       Date:  2000-05       Impact factor: 4.981

3.  Incorporating quality of evidence into decision analytic modeling.

Authors:  R Scott Braithwaite; Mark S Roberts; Amy C Justice
Journal:  Ann Intern Med       Date:  2007-01-16       Impact factor: 25.391

4.  The pharmacoeconomics of HIV disease.

Authors:  L A Lynn; K A Schulman; J M Eisenberg
Journal:  Pharmacoeconomics       Date:  1992-03       Impact factor: 4.981

5.  Data explorer: a prototype expert system for statistical analysis.

Authors:  C Aliferis; E Chao; G F Cooper
Journal:  Proc Annu Symp Comput Appl Med Care       Date:  1993

6.  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.

Authors:  C Elizabeth McCarron; Eleanor M Pullenayegum; Lehana Thabane; Ron Goeree; Jean-Eric Tarride
Journal:  BMC Med Res Methodol       Date:  2010-07-09       Impact factor: 4.615

Review 7.  Ovarian preservation by GnRH agonists during chemotherapy: a meta-analysis.

Authors:  Megan E B Clowse; Millie A Behera; Carey K Anders; Susannah Copland; Cynthia J Coffman; Phyllis C Leppert; Lori A Bastian
Journal:  J Womens Health (Larchmt)       Date:  2009-03       Impact factor: 2.681

Review 8.  Cost-effectiveness analysis in diagnosis of cardiac disease: overview of its rationale and method.

Authors:  R E Patterson
Journal:  J Nucl Cardiol       Date:  1996 Jul-Aug       Impact factor: 5.952

Review 9.  A systematic review and meta-analysis of biological treatments targeting tumour necrosis factor α for sciatica.

Authors:  Nefyn H Williams; Ruth Lewis; Nafees Ud Din; Hosam E Matar; Deborah Fitzsimmons; Ceri J Phillips; Alex Sutton; Kim Burton; Maggie Hendry; Sadia Nafees; Clare Wilkinson
Journal:  Eur Spine J       Date:  2013-03-26       Impact factor: 3.134

10.  Classifying information-sharing methods.

Authors:  Georgios F Nikolaidis; Beth Woods; Stephen Palmer; Marta O Soares
Journal:  BMC Med Res Methodol       Date:  2021-05-22       Impact factor: 4.615

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