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.
Keywords: Bayesian inference; absolute effect; arm-based method; network meta-analysis.