Abstract
Network meta-analysis is a powerful approach for synthesizing direct and indirect evidence about multiple treatment comparisons from a collection of independent studies. At present, the most widely used method in network meta-analysis is contrast-based, in which a baseline treatment needs to be specified in each study, and the analysis focuses on modeling relative treatment effects (typically log odds ratios). However, population-averaged treatment-specific parameters, such as absolute risks, cannot be estimated by this method without an external data source or a separate model for a reference treatment. Recently, an arm-based network meta-analysis method has been proposed, and the R package pcnetmeta provides user-friendly functions for its implementation. This package estimates both absolute and relative effects, and can handle binary, continuous, and count outcomes.
Highlights
In diverse scientific fields, such as social and medical research, summaries of cumulative knowledge are increasingly based on the results of meta-analyses (Hunter and Schmidt 1996; Lindholm et al 2005; Cooper et al 2009)
This article presents an overview of the R package pcnetmeta
Markov chain Monte Carlo (MCMC) convergence diagnostics have been extensively discussed in the literature (Cowles and Carlin 1996; Kass et al 1998)
Summary
Traditional meta-analysis focuses on direct pairwise comparisons between two treatments in the collected studies. Single-arm studies cannot be included in the contrast-based model but they may provide valuable information for treatment comparisons and enhance the robustness of a network meta-analysis (Lin et al 2016a). This article introduces the R package pcnetmeta (Lin et al 2016b), which performs network meta-analysis using the arm-based model and provides estimates for various effect sizes. This package is available from Comprehensive R Archive Network (R Core Team 2015) at http://CRAN.R-project.org/ package=pcnetmeta.
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