Bayesian Network Meta-Analysis of Individual and Aggregate Data
Example newly-diagnosed multiple myeloma
Example plaque psoriasis ML-NMR
Example smoking FE NMA
Example smoking node-splitting
Example smoking RE NMA
Example smoking UME NMA
Target average acceptance probability
Add numerical integration points to aggregate data
Convert samples into arrays, matrices, or data frames
as.stanfit
The Bernoulli Distribution
Combine multiple data sources into one network
Set default values
Deviance Information Criterion (DIC)
Specify a general marginal distribution
The Gamma distribution
Generalised Student's t distribution (with location and scale)
Kaplan-Meier curves of survival data
Direct and indirect evidence
Convert networks to graph objects
Check network connectedness
Log Student's t distribution
The logit Normal distribution
Model comparison using the loo package
Knot locations for M-spline baseline hazard models
Marginal treatment effects
Working with 3D MCMC arrays
Distribution functions for M-spline baseline hazards
Multinomial outcome data
multinma: A Package for Network Meta-Analysis of Individual and Aggreg...
Newly diagnosed multiple myeloma
The nma_data class
The nma_dic class
The nma_nodesplit class
The nma_prior class
The nma_summary class
Methods for nma_summary objects
Network meta-analysis models
The nodesplit_summary class
Methods for nodesplit_summary objects
Matrix of plots for a stan_nma object
Plaque psoriasis data
Plot numerical integration error
Plot prior vs posterior distribution
Network plots
Plots of model fit diagnostics
Plots of summary results
Plots of node-splitting models
Treatment rankings and rank probabilities
Predictions of absolute effects from NMA models
Print nma_data objects
Print DIC details
Print nma_nodesplit_df objects
Print stan_nma objects
Prior distributions
Random effects structure
Objects exported from other packages
Relative treatment effects
Set up arm-based aggregate data
Set up contrast-based aggregate data
Set up aggregate survival data
Set up individual patient data
The stan_nma class
Summarise the results of node-splitting models
Summary of prior distributions
Posterior summaries from stan_nma objects
Plot theme for multinma plots
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'.
Useful links