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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.

Getting started

A good place to start is with the package vignettes which walk through example analyses, see vignette("vignette_overview") for an overview. The series of NICE Technical Support Documents on evidence synthesis gives a detailed introduction to network meta-analysis:

Dias, S. et al. (2011). “NICE DSU Technical Support Documents 1-7: Evidence Synthesis for Decision Making.” National Institute for Health and Care Excellence. Available from http://nicedsu.org.uk/.

Multilevel network meta-regression is set out in the following methods paper:

Phillippo, D. M. et al. (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. doi: 10.1111/rssa.12579.

Citing multinma

The multinma package can be cited as follows:

Phillippo, D. M. (2020). multinma: Network Meta-Analysis of Individual and Aggregate Data in Stan. R package version 0.2.0, doi: 10.5281/zenodo.3904454.

When fitting ML-NMR models, please cite the methods paper:

Phillippo, D. M. et al. (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. doi: 10.1111/rssa.12579.

References

Carpenter, B., A. Gelman, M. D. Hoffman, D. Lee, B. Goodrich, M. Betancourt, M. Brubaker, J. Guo, P. Li, and A. Riddell. 2017. “Stan: A Probabilistic Programming Language.” Journal of Statistical Software 76 (1). https://doi.org/10.18637/jss.v076.i01.

Phillippo, D. M. 2019. “Calibration of Treatment Effects in Network Meta-Analysis Using Individual Patient Data.” PhD thesis, University of Bristol.

Phillippo, D. M., S. Dias, A. E. Ades, M. Belger, A. Brnabic, A. Schacht, D. Saure, Z. Kadziola, and N. 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

0.2.0

License

GPL-3

Issues

17

Pull Requests

4

Stars

36

Forks

24

Maintainer

David Phillippo

Last Published

December 4th, 2020

Functions in multinma (0.2.0)

example_pso_mlnmr

Example plaque psoriasis ML-NMR
as.array.stan_nma

Convert samples into arrays, matrices, or data frames
as.stanfit

as.stanfit
qbern

The Bernoulli Distribution
adapt_delta

Target average acceptance probability
qgamma

The Gamma distribution
add_integration

Add numerical integration points to aggregate data
example_smk_ume

Example smoking UME NMA
example_smk_re

Example smoking RE NMA
example_smk_fe

Example smoking FE NMA
atrial_fibrillation

Stroke prevention in atrial fibrillation patients
dic

Deviance Information Criterion (DIC)
dgent

Generalised Student's t distribution (with location and scale)
distr

Specify a general marginal distribution
blocker

Beta blockers to prevent mortality after MI
combine_network

Combine multiple data sources into one network
dietary_fat

Reduced dietary fat to prevent mortality
diabetes

Incidence of diabetes in trials of antihypertensive drugs
.default

Set default values
is_network_connected

Check network connectedness
qlogitnorm

The logit Normal distribution
loo.stan_nma

Model comparison using the loo package
mcmc_array-class

Working with 3D MCMC arrays
nma_data-class

The nma_data class
nma

Network meta-analysis models
multi

Multinomial outcome data
multinma-package

multinma: A Package for Network Meta-Analysis of Individual and Aggregate Data in Stan
plot.nma_summary

Plots of summary results
plot.nma_dic

Plots of model fit diagnostics
pairs.stan_nma

Matrix of plots for a stan_nma object
print.nma_data

Print nma_data objects
bcg_vaccine

BCG vaccination
print.nma_dic

Print DIC details
RE_cor

Random effects structure
relative_effects

Relative treatment effects
parkinsons

Mean off-time reduction in Parkison's disease
nma_summary-class

The nma_summary class
nma_dic-class

The nma_dic class
print.nma_summary

Methods for nma_summary objects
predict.stan_nma

Predictions of absolute effects from NMA models
theme_multinma

Plot theme for multinma plots
posterior_ranks

Treatment rankings and rank probabilities
plaque_psoriasis_ipd

Plaque psoriasis data
as.igraph.nma_data

Convert networks to graph objects
smoking

Smoking cessation data
set_ipd

Set up individual patient data
hta_psoriasis

HTA Plaque Psoriasis
plot_prior_posterior

Plot prior vs posterior distribution
plot_integration_error

Plot numerical integration error
nma_prior-class

The nma_prior class
priors

Prior distributions
print.stan_nma

Print stan_nma objects
thrombolytics

Thrombolytic treatments data
summary.nma_prior

Summary of prior distributions
summary.stan_nma

Posterior summaries from stan_nma objects
set_agd_arm

Set up arm-based aggregate data
plot.nma_data

Network plots
transfusion

Granulocyte transfusion in patients with neutropenia or neutrophil dysfunction
set_agd_contrast

Set up contrast-based aggregate data
stan_nma-class

The stan_nma class
statins

Statins for cholesterol lowering