Extending DerSimonian and Laird's methodology to perform network meta‐analyses with random inconsistency effects
Year: 2015
Type: article
Abstract: Network meta-analysis is becoming more popular as a way to compare multiple treatments simultaneously. Here, we develop a new estimation method for fitting models for network meta-analysis with random inconsistency effects. This method is an extension of the procedure originally proposed by DerSimonian and Laird. Our methodology allows for inconsistency within the network. The proposed procedure is semi-parametric, non-iterative, fast and highly accessible to applied researchers. The methodology is found to perform satisfactorily in a simulation study provided that the sample size is large enough and the extent of the inconsistency is not very severe. We apply our approach to two real examples.
Source: Statistics in Medicine
DOI: 10.1002/sim.6752
Institution: MRC Biostatistics Unit
Language: English
Subfield: Statistics, Probability and Uncertainty
Field: Decision Sciences
Domain: Social Sciences
Open Access status: hybrid
Estimated APC paid: $5,450
Awards: G1670097818, NIHR Senior Investigator Award, NIHR Senior Investigator Award, G4211044638, G4543806408, G5238254024, G5272147299, Enhancing cardiovascular disease risk prediction through using longitudinal measures of risk factors in clustered data, G6092245007, G6647857051, G7128921068, G8254630708, RENEWAL: Systematic approaches to the evaluation of emerging coronary risk markers. Large-scale epidemiological analyses of existing data and stored biological samples
OpenAlex ID: W2105740276