Title |
Modelling heterogeneity variances in multiple treatment comparison meta-analysis – Are informative priors the better solution?
|
---|---|
Published in |
BMC Medical Research Methodology, January 2013
|
DOI | 10.1186/1471-2288-13-2 |
Pubmed ID | |
Authors |
Kristian Thorlund, Lehana Thabane, Edward J Mills |
Abstract |
Multiple treatment comparison (MTC) meta-analyses are commonly modeled in a Bayesian framework, and weakly informative priors are typically preferred to mirror familiar data driven frequentist approaches. Random-effects MTCs have commonly modeled heterogeneity under the assumption that the between-trial variance for all involved treatment comparisons are equal (i.e., the 'common variance' assumption). This approach 'borrows strength' for heterogeneity estimation across treatment comparisons, and thus, ads valuable precision when data is sparse. The homogeneous variance assumption, however, is unrealistic and can severely bias variance estimates. Consequently 95% credible intervals may not retain nominal coverage, and treatment rank probabilities may become distorted. Relaxing the homogeneous variance assumption may be equally problematic due to reduced precision. To regain good precision, moderately informative variance priors or additional mathematical assumptions may be necessary. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 1 | 100% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 1 | 100% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Japan | 1 | 2% |
Netherlands | 1 | 2% |
United States | 1 | 2% |
Unknown | 51 | 94% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 11 | 20% |
Researcher | 10 | 19% |
Student > Master | 5 | 9% |
Professor | 5 | 9% |
Student > Doctoral Student | 3 | 6% |
Other | 12 | 22% |
Unknown | 8 | 15% |
Readers by discipline | Count | As % |
---|---|---|
Medicine and Dentistry | 16 | 30% |
Mathematics | 13 | 24% |
Business, Management and Accounting | 2 | 4% |
Economics, Econometrics and Finance | 2 | 4% |
Agricultural and Biological Sciences | 2 | 4% |
Other | 7 | 13% |
Unknown | 12 | 22% |