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A simple regression model for network meta-analysis


Kessels, A G H; ter Riet, G; Puhan, Milo A; Kleijnen, J; Bachmann, L M; Minder, C (2013). A simple regression model for network meta-analysis. OA Epidemiology, 1(1):7.

Abstract

Introduction: The aim of this paper is to propose a transparent, alternative approach for network meta-analysis based on a regression model that allows inclusion of studies with three or more treatment arms.
Methodology: Based on the contingency tables describing the frequency distribution of the outcome in the different intervention arms, a data set is constructed. A logistic regression is used to determine the parameters describing the difference in effect between a specific intervention and the reference intervention and to check the assumptions needed to model the effect parameters. The method is demonstrated by re-analysing 24 studies investigating the effect of smoking cessation interventions. The results of the analysis were similar to two other published approaches to network analysis using the same data set. The presence of heterogeneity, including inconsistency, was examined.
Conclusion: The proposed method provides an easy and transparent way to estimate treatment effect parameters in metaanalyses involving studies with more than two arms. It has several additional attractive features such as not overweighting small studies as the random effect models do, dealing with zero count cells, checking of assumptions about the distribution of model parameters and investigation of heterogeneity across trials and between direct and indirect evidence.

Introduction: The aim of this paper is to propose a transparent, alternative approach for network meta-analysis based on a regression model that allows inclusion of studies with three or more treatment arms.
Methodology: Based on the contingency tables describing the frequency distribution of the outcome in the different intervention arms, a data set is constructed. A logistic regression is used to determine the parameters describing the difference in effect between a specific intervention and the reference intervention and to check the assumptions needed to model the effect parameters. The method is demonstrated by re-analysing 24 studies investigating the effect of smoking cessation interventions. The results of the analysis were similar to two other published approaches to network analysis using the same data set. The presence of heterogeneity, including inconsistency, was examined.
Conclusion: The proposed method provides an easy and transparent way to estimate treatment effect parameters in metaanalyses involving studies with more than two arms. It has several additional attractive features such as not overweighting small studies as the random effect models do, dealing with zero count cells, checking of assumptions about the distribution of model parameters and investigation of heterogeneity across trials and between direct and indirect evidence.

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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:04 Faculty of Medicine > Epidemiology, Biostatistics and Prevention Institute (EBPI)
04 Faculty of Medicine > University Hospital Zurich > Clinic and Policlinic for Internal Medicine
Dewey Decimal Classification:610 Medicine & health
Language:English
Date:22 July 2013
Deposited On:16 Jan 2014 13:10
Last Modified:05 Apr 2016 17:25
Publisher:Open Access Publishing London
ISSN:2053-079X
Free access at:Official URL. An embargo period may apply.
Official URL:http://www.oapublishinglondon.com/article/690
Permanent URL: https://doi.org/10.5167/uzh-88887

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