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, populationaveraged 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.
Bibliographical noteFunding Information:
LL and JZ were supported in part by the NIAID AI103012. HC was supported in part by the NIAID AI103012, NIDCR R03DE024750, NLM R21LM012197, NCI P30CA077598, NIMHD U54MD008620, and NIDDK U01DK106786.
© 2017, American Statistical Association. All rights reserved.
- Absolute effect
- Arm-based method
- Bayesian inference
- Network meta-analysis