Package: multinma 0.9.1.9002

multinma: 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'.

Authors:David M. Phillippo [aut, cre], Samuel J. Perren [ctb], Niels Dunnewind [ctb]

multinma_0.9.1.9002.tar.gz
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manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
multinma/json (API)

# Install 'multinma' in R:
install.packages('multinma', repos = c('https://dmphillippo.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/dmphillippo/multinma/issues

Pkgdown/docs site:https://dmphillippo.github.io

Uses libs:
  • c++– GNU Standard C++ Library v3
Datasets:

On CRAN:

Conda:

statisticscpp

9.73 score 46 stars 286 scripts 1.3k downloads 1 mentions 65 exports 96 dependencies

Last updated from:8489bd83f3. Checks:12 OK, 1 FAIL. Indexed: yes.

TargetResultTimeFilesSyslog
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source / vignettesOK1512
linux-release-arm64OK970
linux-release-x86_64OK1036
macos-release-arm64OK788
macos-release-x86_64OK1677
macos-oldrel-arm64OK1116
macos-oldrel-x86_64OK1679
windows-develOK1755
windows-releaseOK1772
windows-oldrelOK1755
wasm-releaseFAIL233

Exports:.default.is_defaultadd_integrationas.stanfitas.tibble.nodesplit_summarybind_chainscauchycombine_networkdberndgammadgentdicdistrdlogitnormdlogtdmsplineexponentialflatgeom_kmget_nodesplitshalf_cauchyhalf_normalhalf_student_thas_directhas_indirecthmsplineHmsplineinv_softmaxis_network_connectedlog_normallog_student_tmake_knotsmarginal_effectsmultinmanormalpbernpgammapgentplogitnormplogtplot_integration_errorplot_prior_posteriorpmsplineposterior_rank_probsposterior_ranksqbernqgammaqgentqlogitnormqlogtqmsplineRE_correlative_effectsrmst_msplineset_agd_armset_agd_contrastset_agd_survset_ipdsoftmaxstudent_tSurvtheme_multinmaunnest_integrationwhich_RE

Dependencies:abindADGofTestbackportsbase64encbayesplotBHcachemcallrcheckmatecliclustercolorspacecopulacpp11descdistributionaldplyrevdfarverfastmapforcatsgenericsggdistggforceggplot2ggraphggrepelggridgesgluegraphlayoutsgridExtragslgtableigraphinlineisobandjsonlitelabelinglatticelifecycleloomagrittrMASSMatrixmatrixStatsmemoisemvtnormnumDerivotelpcaPPpillarpkgbuildpkgconfigplyrpolyclipposteriorprocessxpspsplinepurrrquadprogQuickJSRR6randtoolboxrbibutilsRColorBrewerRcppRcppArmadilloRcppEigenRcppParallelRdpackreshape2rlangrngWELLrstanrstantoolsS7scalesstabledistStanHeadersstringistringrsurvivalsystemfontstensorAtibbletidygraphtidyrtidyselecttruncdisttweenrutf8vctrsviridisviridisLitewithr

Overview of Examples
References
Rendered fromvignette_overview.Rmdusingknitr::rmarkdown

Last update: 2026-02-02
Started: 2020-11-20

Example: Certolizumab
Setting up the network | Meta-analysis models | Further results | References
Rendered fromexample_certolizumab.html.asisusingR.rsp::asis

Last update: 2026-02-02
Started: 2026-02-02

Example: Social Anxiety
Setting up the network | Model selection strategy | STEP 1: Heterogeniety assessement | STEP 2: Consistency assessment | STEP 3: Class effects assessment | STEP 4: Class Model Assessment | Results for final chosen model | Relative treatment effects | Relative class effects | References
Rendered fromexample_social_anxiety.html.asisusingR.rsp::asis

Last update: 2025-03-21
Started: 2025-03-21

Example: Newly diagnosed multiple myeloma
Example: Newly diagnosed multiple myeloma | Study data | Setup | Preparing treatment classes | Setting up the network | Adding numerical integration for ML-NMR | Network plot | Kaplan-Meier plots | ML-NMR models with M-spline baseline hazards | Ploting hazards | Assessing the proportional hazards assumption | Comparison to unadjusted NMA | Producing population-average estimates | Population-average survival probabilities | Population-average median survival times | Population-average conditional log hazard ratios | Population-average marginal hazard ratios | References
Rendered fromexample_ndmm.html.asisusingR.rsp::asis

Last update: 2023-08-30
Started: 2023-08-30

Example: Atrial fibrillation
Setting up the network | Meta-analysis models | Model fit and comparison | References
Rendered fromexample_atrial_fibrillation.html.asisusingR.rsp::asis

Last update: 2020-11-26
Started: 2020-06-18

Example: BCG vaccine for tuberculosis
Setting up the network | Meta-analysis models | Model fit and comparison | Further results | References
Rendered fromexample_bcg_vaccine.html.asisusingR.rsp::asis

Last update: 2020-11-26
Started: 2020-06-18

Example: Beta blockers
Setting up the network | Meta-analysis models | Further results | References
Rendered fromexample_blocker.html.asisusingR.rsp::asis

Last update: 2020-11-26
Started: 2020-06-18

Example: Dietary fat
Setting up the network | Meta-analysis models | Further results | References
Rendered fromexample_dietary_fat.html.asisusingR.rsp::asis

Last update: 2020-11-26
Started: 2020-06-18

Example: Parkinson's disease
Example: Parkinson’s disease | Analysis of arm-based data | Analysis of contrast-based data | Analysis of mixed arm-based and contrast-based data | References
Rendered fromexample_parkinsons.html.asisusingR.rsp::asis

Last update: 2020-11-26
Started: 2020-06-18

Example: Plaque psoriasis HTA report
Setting up the network | Meta-analysis models | Further results | References
Rendered fromexample_hta_psoriasis.html.asisusingR.rsp::asis

Last update: 2020-11-26
Started: 2020-09-11

Example: Plaque psoriasis ML-NMR
Initial analysis | Setup | ML-NMR models | Model comparison | Producing relative effects and event probabilities | Extended analysis | ML-NMR model | Assessing assumptions | References
Rendered fromexample_plaque_psoriasis.html.asisusingR.rsp::asis

Last update: 2020-11-26
Started: 2020-06-18

Example: Smoking cessation
Setting up the network | Random effects NMA | Checking for inconsistency | Further results | References
Rendered fromexample_smoking.html.asisusingR.rsp::asis

Last update: 2020-11-26
Started: 2020-06-18

Example: Thrombolytic treatments
Setting up the network | Fixed effects NMA | Checking for inconsistency | Further results | References
Rendered fromexample_thrombolytics.html.asisusingR.rsp::asis

Last update: 2020-11-26
Started: 2020-06-18

Example: White blood cell transfusion
Setting up the network | Meta-analysis models | References
Rendered fromexample_transfusion.html.asisusingR.rsp::asis

Last update: 2020-11-26
Started: 2020-06-18

Example: Diabetes
Setting up the network | Meta-analysis models | Further results | References
Rendered fromexample_diabetes.html.asisusingR.rsp::asis

Last update: 2020-06-18
Started: 2020-06-18

Example: Statins for cholesterol lowering
Example: Statins for cholesterol lowering | Setting up the network | Meta-analysis models | Model fit and comparison | Further results | References
Rendered fromexample_statins.html.asisusingR.rsp::asis

Last update: 2020-06-18
Started: 2020-06-18

Readme and manuals

Help Manual

Help pageTopics
multinma: A Package for Network Meta-Analysis of Individual and Aggregate Data in Stanmultinma-package multinma
Set default values.default .is_default
Target average acceptance probabilityadapt_delta
Add numerical integration points to aggregate dataadd_integration add_integration.data.frame add_integration.default add_integration.nma_data unnest_integration
Convert samples into arrays, matrices, or data framesas.array.stan_nma as.data.frame.stan_nma as.matrix.stan_nma as.tibble.stan_nma as_tibble.stan_nma
Convert networks to graph objectsas.igraph.nma_data as_tbl_graph.nma_data
as.stanfitas.stanfit as.stanfit.default as.stanfit.stan_nma
Stroke prevention in atrial fibrillation patientsatrial_fibrillation
BCG vaccinationbcg_vaccine
Bind chainsbind_chains cbind.mcmc_array cbind.stan_nma
Beta blockers to prevent mortality after MIblocker
Certolizumabcertolizumab
Combine multiple data sources into one networkcombine_network
Generalised Student's t distribution (with location and scale)dgent pgent qgent
Incidence of diabetes in trials of antihypertensive drugsdiabetes
Deviance Information Criterion (DIC)dic
Reduced dietary fat to prevent mortalitydietary_fat
Specify a general marginal distributiondistr
The logit Normal distributiondlogitnorm plogitnorm qlogitnorm
Log Student's t distributiondlogt plogt qlogt
Distribution functions for M-spline baseline hazardsdmspline Hmspline hmspline pmspline qmspline rmst_mspline
Example newly-diagnosed multiple myelomaexample_ndmm
Example plaque psoriasis ML-NMRexample_pso_mlnmr
Example smoking FE NMAexample_smk_fe
Example smoking node-splittingexample_smk_nodesplit
Example smoking RE NMAexample_smk_re
Example smoking UME NMAexample_smk_ume
Kaplan-Meier curves of survival datageom_km
Direct and indirect evidenceget_nodesplits has_direct has_indirect
HTA Plaque Psoriasishta_psoriasis
Check network connectednessis_network_connected
Knot locations for a fitted modelknots.stan_nma
Model comparison using the 'loo' packageloo loo.stan_nma waic waic.stan_nma
Knot locations for M-spline baseline hazard modelsmake_knots
Marginal treatment effectsmarginal_effects
Working with 3D MCMC arraysmcmc_array mcmc_array-class names.mcmc_array names<-.mcmc_array plot.mcmc_array print.mcmc_array summary.mcmc_array
Multinomial outcome datamulti
Newly diagnosed multiple myelomandmm_agd ndmm_agd_covs ndmm_ipd
Network meta-analysis modelsnma
The nma_data classmlnmr_data mlnmr_data-class nma_data nma_data-class
The nma_dic classnma_dic nma_dic-class
The nma_nodesplit classnma_nodesplit nma_nodesplit-class nma_nodesplit_df nma_nodesplit_df-class
The nma_prior classnma_prior nma_prior-class
The 'nma_summary' classnma_rank_probs nma_summary nma_summary-class
The 'nodesplit_summary' classnodesplit_summary nodesplit_summary-class
Matrix of plots for a 'stan_nma' objectpairs.stan_nma
Mean off-time reduction in Parkison's diseaseparkinsons
Plaque psoriasis dataplaque_psoriasis plaque_psoriasis_agd plaque_psoriasis_ipd
Plot numerical integration errorplot_integration_error
Plot prior vs posterior distributionplot_prior_posterior
Network plotsplot.nma_data
Plots of model fit diagnosticsplot.nma_dic
Plots of summary resultsplot.nma_parameter_summary plot.nma_rank_probs plot.nma_summary plot.surv_nma_summary
Plots of node-splitting modelsplot.nodesplit_summary
Treatment rankings and rank probabilitiesposterior_ranks posterior_rank_probs
Predictions of absolute effects from NMA modelspredict.stan_nma predict.stan_nma_surv
Print 'nma_data' objectsprint.mlnmr_data print.nma_data
Methods for 'nma_dic' objectsas.array.nma_dic as.data.frame.nma_dic as.matrix.nma_dic as.tibble.nma_dic as_tibble.nma_dic print.nma_dic
Print 'nma_nodesplit_df' objectsprint.nma_nodesplit print.nma_nodesplit_df
Methods for 'nma_summary' objectsas.array.nma_rank_probs as.array.nma_summary as.data.frame.nma_summary as.matrix.nma_rank_probs as.matrix.nma_summary as.tibble.nma_summary as_tibble.nma_summary print.nma_summary
Methods for 'nodesplit_summary' objectsas.data.frame.nodesplit_summary as.tibble.nodesplit_summary as_tibble.nodesplit_summary print.nodesplit_summary
Print 'stan_nma' objectsprint.stan_nma
Prior distributionscauchy exponential flat half_cauchy half_normal half_student_t log_normal log_student_t normal priors student_t
The Bernoulli Distributiondbern pbern qbern
The Gamma distributiondgamma pgamma qgamma
Random effects structureRE_cor which_RE
Relative treatment effectsrelative_effects
Set up arm-based aggregate dataset_agd_arm
Set up contrast-based aggregate dataset_agd_contrast
Set up aggregate survival dataset_agd_surv
Set up individual patient dataset_ipd
Smoking cessation datasmoking
Social Anxietysocial_anxiety
Softmax transforminv_softmax softmax
The stan_nma classstan_mlnmr stan_nma stan_nma-class
Statins for cholesterol loweringstatins
Summarise the results of node-splitting modelsplot.nma_nodesplit plot.nma_nodesplit_df summary.nma_nodesplit summary.nma_nodesplit_df
Summary of prior distributionssummary.nma_prior
Posterior summaries from 'stan_nma' objectsplot.stan_nma summary.stan_nma
Plot theme for multinma plotstheme_multinma
Thrombolytic treatments datathrombolytics
Granulocyte transfusion in patients with neutropenia or neutrophil dysfunctiontransfusion