multinma v0.1.3


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Bayesian Network Meta-Analysis of Individual and Aggregate Data

Network meta-analysis and network meta-regression models for aggregate data, individual patient data, and mixtures of both individual and aggregate data using multilevel network meta-regression as described by Phillippo et al. (2020) <doi:10.1111/rssa.12579>. Models are estimated in a Bayesian framework using 'Stan'.


multinma: Network Meta-Analysis of individual and aggregate data in Stan

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The multinma package implements network meta-analysis, network meta-regression, and multilevel network meta-regression models which combine evidence from a network of studies and treatments using either aggregate data or individual patient data from each study (Phillippo et al. 2020; Phillippo 2019). Models are estimated in a Bayesian framework using Stan (Carpenter et al. 2017).


You can install the released version of multinma from CRAN with:


And the development version from GitHub with:

# install.packages("devtools")

Installing from source (either from CRAN or GitHub) requires that the rstan package is installed and configured. See the installation guide here.


Carpenter, Bob, Andrew Gelman, Matthew D. Hoffman, Daniel Lee, Ben Goodrich, Michael Betancourt, Marcus Brubaker, Jiqiang Guo, Peter Li, and Allen Riddell. 2017. “Stan: A Probabilistic Programming Language.” Journal of Statistical Software 76 (1).
Phillippo, David Mark. 2019. “Calibration of Treatment Effects in Network Meta-Analysis Using Individual Patient Data.” PhD thesis, University of Bristol.
Phillippo, David M., Sofia Dias, A. E. Ades, Mark Belger, Alan Brnabic, Alexander Schacht, Daniel Saure, Zbigniew Kadziola, and Nicky J. Welton. 2020. “Multilevel Network Meta-Regression for Population-Adjusted Treatment Comparisons.” Journal of the Royal Statistical Society: Series A (Statistics in Society) 183 (3): 1189–1210.

Functions in multinma

Name Description
qgamma The Gamma distribution
combine_network Combine multiple data sources into one network
blocker Beta blockers to prevent mortality after MI
add_integration Add numerical integration points to aggregate data
as.stanfit as.stanfit
as.array.stan_nma Convert samples into arrays, matrices, or data frames
adapt_delta Target average acceptance probability
qbern The Bernoulli Distribution
distr Specify a general marginal distribution
atrial_fibrillation Stroke prevention in atrial fibrillation patients
dgent Generalised Student's t distribution (with location and scale)
multinma-package multinma: A Package for Network Meta-Analysis of Individual and Aggregate Data in Stan
is_network_connected Check network connectedness
mcmc_array-class Working with 3D MCMC arrays
as.igraph.nma_data Convert networks to graph objects
nma_summary-class The nma_summary class
diabetes Incidence of diabetes in trials of antihypertensive drugs
print.nma_summary Methods for nma_summary objects
.default Set default values
dic Deviance Information Criterion (DIC)
nma_dic-class The nma_dic class
bcg_vaccine BCG vaccination
qlogitnorm The logit Normal distribution
dietary_fat Reduced dietary fat to prevent mortality
nma Network meta-analysis models
pairs.stan_nma Matrix of plots for a stan_nma object
loo.stan_nma Model comparison using the loo package
parkinsons Mean off-time reduction in Parkison's disease
plot.nma_summary Plots of summary results
plot.nma_dic Plots of model fit diagnostics
nma_data-class The nma_data class
predict.stan_nma Predictions of absolute effects from NMA models
posterior_ranks Treatment rankings and rank probabilities
nma_prior-class The nma_prior class
plaque_psoriasis_ipd Plaque psoriasis data
print.nma_data Print nma_data objects
RE_cor Random effects structure
relative_effects Relative treatment effects
plot.nma_data Network plots
transfusion Granulocyte transfusion in patients with neutropenia or neutrophil dysfunction
print.stan_nma Print stan_nma objects
priors Prior distributions
plot_integration_error Plot numerical integration error
set_agd_arm Set up arm-based aggregate data
plot_prior_posterior Plot prior vs posterior distribution
set_agd_contrast Set up contrast-based aggregate data
set_ipd Set up individual patient data
thrombolytics Thrombolytic treatments data
theme_multinma Plot theme for multinma plots
summary.nma_prior Summary of prior distributions
smoking Smoking cessation data
summary.stan_nma Posterior summaries from stan_nma objects
print.nma_dic Print DIC details
stan_nma-class The stan_nma class
statins Statins for cholesterol lowering
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License GPL-3
Encoding UTF-8
LazyData true
Biarch true
LinkingTo BH (>= 1.66.0), Rcpp (>= 0.12.0), RcppEigen (>=, rstan (>= 2.18.1), StanHeaders (>= 2.18.0)
SystemRequirements GNU make
RoxygenNote 7.1.0
RdMacros Rdpack
VignetteBuilder knitr, R.rsp
NeedsCompilation yes
Packaged 2020-06-29 16:14:48 UTC; dp14189
Repository CRAN
Date/Publication 2020-06-30 09:20:11 UTC

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