Function reference • multinma

Package overview

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

Defining a network

Setting up a network from different data sources, creating network plots.

set_agd_arm(): Set up arm-based aggregate data

set_agd_contrast(): Set up contrast-based aggregate data

set_agd_surv(): Set up aggregate survival data

set_ipd(): Set up individual patient data

combine_network(): Combine multiple data sources into one network

multi(): Multinomial outcome data

print(<nma_data>) print(<mlnmr_data>): Print nma_data objects

plot(<nma_data>): Network plots

as.igraph(<nma_data>) as_tbl_graph(<nma_data>): Convert networks to graph objects

nma_data-class nma_data mlnmr_data mlnmr_data-class: The nma_data class

is_network_connected(): Check network connectedness

Setting up numerical integration (ML-NMR only)

Multilevel network meta-regression models require numerical integration points to be specified for the distributions of covariates in each aggregate data study in the network.

add_integration() unnest_integration(): Add numerical integration points to aggregate data

distr(): Specify a general marginal distribution

qbern() pbern() dbern(): The Bernoulli Distribution

qgamma() dgamma() pgamma(): The Gamma distribution

dgent() pgent() qgent(): Generalised Student's t distribution (with location and scale)

dlogt() plogt() qlogt(): Log Student's t distribution

qlogitnorm() dlogitnorm() plogitnorm(): The logit Normal distribution

Prior distributions

Specify and summarise prior distributions.

normal() half_normal() log_normal() cauchy() half_cauchy() student_t() half_student_t() log_student_t() exponential() flat(): Prior distributions

summary(<nma_prior>): Summary of prior distributions

nma_prior-class nma_prior: The nma_prior class

plot_prior_posterior(): Plot prior vs posterior distribution

dgent() pgent() qgent(): Generalised Student's t distribution (with location and scale)

dlogt() plogt() qlogt(): Log Student's t distribution

Model fitting

Model specification and fitting is accomplished using the nma() function.

nma(): Network meta-analysis models

print(<stan_nma>): Print stan_nma objects

summary(<stan_nma>) plot(<stan_nma>): Posterior summaries from stan_nma objects

pairs(<stan_nma>): Matrix of plots for a stan_nma object

stan_nma-class stan_nma stan_mlnmr: The stan_nma class

adapt_delta: Target average acceptance probability

RE_cor() which_RE(): Random effects structure

.default() .is_default(): Set default values

Model checking and comparison

Checking model fit and comparing models.

plot_prior_posterior(): Plot prior vs posterior distribution

plot_integration_error(): Plot numerical integration error

dic(): Deviance Information Criterion (DIC)

print(<nma_dic>): Print DIC details

plot(<nma_dic>): Plots of model fit diagnostics

nma_dic-class nma_dic: The nma_dic class

loo(<stan_nma>) waic(<stan_nma>): Model comparison using the loo package

Node-splitting

Generate and summarise node-splitting models for assessing inconsistency.

get_nodesplits() has_direct() has_indirect(): Direct and indirect evidence

nma_nodesplit-class nma_nodesplit nma_nodesplit_df nma_nodesplit_df-class: The nma_nodesplit class

print(<nma_nodesplit_df>) print(<nma_nodesplit>): Print nma_nodesplit_df objects

summary(<nma_nodesplit_df>) summary(<nma_nodesplit>) plot(<nma_nodesplit>) plot(<nma_nodesplit_df>): Summarise the results of node-splitting models

nodesplit_summary-class nodesplit_summary: The nodesplit_summary class

print(<nodesplit_summary>) as_tibble(<nodesplit_summary>) as.tibble.nodesplit_summary() as.data.frame(<nodesplit_summary>): Methods for nodesplit_summary objects

plot(<nodesplit_summary>): Plots of node-splitting models

Posterior summaries and working with fitted models

Producing and plotting relative effects, absolute predictions, posterior ranks and rank probabilities. Converting to MCMC arrays and matrices.

relative_effects(): Relative treatment effects

predict(<stan_nma>) predict(<stan_nma_surv>): Predictions of absolute effects from NMA models

posterior_ranks() posterior_rank_probs(): Treatment rankings and rank probabilities

print(<nma_summary>) as.data.frame(<nma_summary>) as.tibble(<nma_summary>) as_tibble(<nma_summary>) as.array(<nma_summary>) as.matrix(<nma_summary>) as.array(<nma_rank_probs>) as.matrix(<nma_rank_probs>): Methods for nma_summary objects

plot(<nma_summary>) plot(<nma_parameter_summary>) plot(<nma_rank_probs>) plot(<surv_nma_summary>): Plots of summary results

nma_summary-class nma_summary nma_rank_probs: The nma_summary class

as.array(<stan_nma>) as.data.frame(<stan_nma>) as_tibble(<stan_nma>) as.tibble(<stan_nma>) as.matrix(<stan_nma>): Convert samples into arrays, matrices, or data frames

summary(<mcmc_array>) print(<mcmc_array>) plot(<mcmc_array>) names(<mcmc_array>) `names<-`(<mcmc_array>): Working with 3D MCMC arrays

as.stanfit(): as.stanfit

M-spline hazards

Functions for flexibile M-splines on the baseline hazard.

dmspline() pmspline() qmspline() hmspline() Hmspline() rmst_mspline(): Distribution functions for M-spline baseline hazards

make_knots(): Knot locations for M-spline baseline hazard models

ggplot functions

Functions for creating or customising ggplot outputs.

theme_multinma(): Plot theme for multinma plots

geom_km(): Kaplan-Meier curves of survival data

Datasets

Datasets used for examples and vignettes.

atrial_fibrillation: Stroke prevention in atrial fibrillation patients

bcg_vaccine: BCG vaccination

blocker: Beta blockers to prevent mortality after MI

diabetes: Incidence of diabetes in trials of antihypertensive drugs

dietary_fat: Reduced dietary fat to prevent mortality

hta_psoriasis: HTA Plaque Psoriasis

ndmm_ipd ndmm_agd ndmm_agd_covs: Newly diagnosed multiple myeloma

parkinsons: Mean off-time reduction in Parkison's disease

plaque_psoriasis_ipd plaque_psoriasis_agd: Plaque psoriasis data

smoking: Smoking cessation data

statins: Statins for cholesterol lowering

thrombolytics: Thrombolytic treatments data

transfusion: Granulocyte transfusion in patients with neutropenia or neutrophil dysfunction

example_ndmm: Example newly-diagnosed multiple myeloma

example_pso_mlnmr: Example plaque psoriasis ML-NMR

example_smk_fe: Example smoking FE NMA

example_smk_nodesplit: Example smoking node-splitting

example_smk_re: Example smoking RE NMA

example_smk_ume: Example smoking UME NMA