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

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).

Installation

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

install.packages("multinma")

And the development version from GitHub with:

# install.packages("devtools")
devtools::install_github("dmphillippo/multinma")

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

References

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). https://doi.org/10.18637/jss.v076.i01.

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. https://doi.org/10.1111/rssa.12579.

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Version

Install

install.packages('multinma')

Monthly Downloads

919

Version

0.1.3

License

GPL-3

Issues

17

Pull Requests

4

Stars

36

Forks

24

Maintainer

David Phillippo

Last Published

June 30th, 2020

Functions in multinma (0.1.3)

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