nmaINLA: nmaINLA: Network meta-analysis using INLA

Description Details Author(s) Source

Description

An R package for performing network meta-analysis using INLA.

Details

Network meta-analysis is a generalization of pairwise meta-analysis to analyze networks of trials comparing two or more treatments simultaneously (Dias et al, 2011). Bayesian hierarchical models are commonly used for network meta-analysis (Dias et al, 2011). The default choice for performing inference within such models are Markov Chain Monte Carlo (MCMC), for example using BUGS-variants programs such as JAGS. A deterministic approach to do fully Bayesian inference for latent Gaussian models (LGMs) are integrated nested Laplace approximations (INLA) (Rue et al, 2009) which is a fast and accurate alternative to MCMC. INLA methodology is implemented as an R package INLA (<www.r-inla.org>). Sauter and Held (2015) has shown that INLA can be used for fitting many NMA models including fixed effect and consistency models, node-splitting models.

This package extends the INLA implementation of Sauter and Held (2015) to Jackson model (Jackson et al, 2014) and network meta-regression and extracts the features needed for NMA models from INLA R package and presents in an intuitive way (Guenhan et al, in preparation). Currently, contrast-based network meta-analysis using trial-arm level data for datasets with binary, continuous, and survival outcomes are supported. Note that the installation of R package 'INLA' is compulsory for successful usage. The 'INLA' package can be obtained from <http://www.r-inla.org>. We recommend the testing version, which can be downloaded by running: source("http://www.math.ntnu.no/inla/givemeINLA-testing.R").

Type vignette("nmaINLA") to how to use this package.

The development version of nmaINLA is available on GitHub <https://github.com/gunhanb/nmaINLA>.

Author(s)

Burak Kuersad Guenhan <[email protected]>

Source

Guenhan, B.K., Friede, T., Held, L. (2018) A design-by-treatment interaction model for network meta-analysis and meta-regression with integrated nested Laplace approximations. Res Syn Meth. 2018;1-14. https://doi.org/10.1002/jrsm.1285

Sauter, R. and Held, L. (2015). Network meta-analysis with integrated nested Laplace approximations. Biometrical Journal 57 1038–1050.

Jackson, D., Barrett, J. K., Rice, S., White, I. R. and Higgins, J. P. (2014). A design-by-treatment interaction model for network meta-analysis with random inconsistency effects. Statistics in Medicine 33 3639–3654.

Rue, H., Martino, S. and Chopin, N. (2009). Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations. Journal of the Royal Statistical Society: Series B (Statistical Methodology) 71 319–392.

Dias, S., Welton, N. J., Sutton, A. J. and Ades, A. (2011). NICE DSU Technical Support Document 2: A Generalised Linear Modelling Framework for Pairwise and Network Meta-analysis of Randomised Controlled Trials. Last updated September 2016.

Dias, S., Sutton, A. J., Welton, N. J. and Ades, A. E. (2013). Evidence synthesis for Decision Making 3: Heterogeneity–Subgroups, Meta-Regression, Bias, and Bias-Adjustment. Medical Decision Making 33 618–640.


gunhanb/nmainla documentation built on July 23, 2018, 3:28 p.m.