Description Details Author(s) References

Provides functions to perform arm-based network meta-analysis for datasets with binary, continuous, and count outcomes.

Currently, much effort in network meta-analysis has been devoted to contrast-based methods, which focus on modeling the relative treatment effects, such as log odds ratio. However, in some situations, patient-centered treatment-specific parameters, such as absolute risk, are preferred. For example, when the outcome is binary, consider two scenarios comparing treatments A and B according to one-year survival rates: (i) *π_{A} = 0.8* vs *π_{B} = 0.5* and (ii) *π_{A} = 0.004* vs *π_{B} = 0.001*. Both scenarios yield an odds ratio 4.0, but patients would prefer treatment A in scenario (i) more strongly than in scenario (ii). The contrast-based network meta-analysis requires external data sources or separate modeling to estimate treatment-specific parameters.

Alternatively, arm-based network meta-analysis focuses on estimating treatment-specific parameters, and relative effects can be subsequently obtained. The arm-based models for binary outcomes are discussed in Salanti et al (2008) and Zhang et al (2014).

This package provides user-friendly functions for arm-based network meta-analysis. Function `nma.ab.bin`

uses the model in Zhang et al (2014) for binary outcomes; it provides estimates for absolute risk (AR), risk ratio (RR), risk difference (RD), odds ratio (OR), log RR, and log OR. This package also handles continuous outcomes and count data.

Parameter estimation in arm-based Bayesian hierarchical models are performed through JAGS. Note that this package does not include a copy of JAGS library, so users must install JAGS separately. Please refer to the JAGS home page at http://mcmc-jags.sourceforge.net/ for instructions on downloading and installing JAGS.

Lifeng Lin, Jing Zhang, and Haitao Chu

Maintainer: Lifeng Lin <[email protected]>

Dias S, Sutton AJ, Ades AE, and Welton NJ (2013). "Evidence synthesis for decision making 2: A generalized linear modeling framework for pairwise and network meta-analysis of randomized controlled trials." *Med Decis Making* **33**(5), 607–17.

Lin L, Zhang J, Hodges JS, and Chu H (2017). "pcnetmeta: An R package for arm-based network meta-analysis." *J Stat Softw*, **80**(5), 1–25.

Lu G and Ades AE (2004). "Combination of direct and indirect evidence in mixed treatment comparisons." *Stat Med* **23**(20), 3105–24.

Salanti G, Higgins JP, Ades AE, and Ioannidis JPA (2008). "Evaluation of networks of randomized trials." *Stat Methods Med Res* **17**(3), 279–301.

Zhang J, Carlin BP, Neaton JD, Soon GG, Nie L, Kane R, Virnig BA, and Chu H (2014). "Network meta-analysis of randomized clinical trials: Reporting the proper summaries." *Clin Trials* **11**(2), 246–62.

Embedding an R snippet on your website

Add the following code to your website.

For more information on customizing the embed code, read Embedding Snippets.