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.
Highlights
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
Given the likelihood function based on the data collected in the studies, this method assumes a number of prior distributions which generate the posterior distribution of the effect parameters
We propose an approach based on an ordinary regression model in which all available information is pooled with respect to the difference in effect of two or more treatments
Summary
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. Including indirect comparisons in meta-analysis can be of value when evidence from head-to-head comparisons is not or only sparsely available, to increase the precision of any metaanalysis of the direct comparisons or to formally rank the benefits of a larger set of different treatments[1,2]. An even more imperative reason for inclusion of indirect comparisons is that valid data should not be ignored and like all other pertinent evidence should be included in the meta- analysis. To compare the effects of more than two competing treatments, several methods have been proposed to pool all available evidence in one network analysis. Given the likelihood function based on the data collected in the studies, this method assumes a number of prior distributions which generate the posterior distribution of the effect parameters
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