Package: MBNMAtime 0.2.6

Hugo Pedder

MBNMAtime: Run Time-Course Model-Based Network Meta-Analysis (MBNMA) Models

Fits Bayesian time-course models for model-based network meta-analysis (MBNMA) that allows inclusion of multiple time-points from studies. Repeated measures over time are accounted for within studies by applying different time-course functions, following the method of Pedder et al. (2019) <doi:10.1002/jrsm.1351>. The method allows synthesis of studies with multiple follow-up measurements that can account for time-course for a single or multiple treatment comparisons. Several general time-course functions are provided; others may be added by the user. Various characteristics can be flexibly added to the models, such as correlation between time points and shared class effects. The consistency of direct and indirect evidence in the network can be assessed using unrelated mean effects models and/or by node-splitting.

Authors:Hugo Pedder [aut, cre]

MBNMAtime_0.2.6.tar.gz
MBNMAtime_0.2.6.tar.gz(r-4.7-any)MBNMAtime_0.2.6.tar.gz(r-4.6-any)
MBNMAtime_0.2.6.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
MBNMAtime/json (API)
NEWS

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

Bug tracker:https://github.com/hugaped/mbnmatime/issues

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

Uses libs:
  • jags– Just Another Gibbs Sampler for Bayesian MCMC - binary JAGS is Just Another Gibbs Sampler. It is a program for analysis of Bayesian hierarchical models using Markov Chain Monte Carlo (MCMC) simulation not wholly unlike BUGS. JAGS was written with three aims in mind: * To have an engine for the BUGS language that runs on Unix * To be extensible, allowing users to write their own functions, distributions and samplers. * To be a plaftorm for experimentation with ideas in Bayesian modelling This package contains the 'jags' binary as well as the associated shared library modules loaded by the binary.
  • c++– GNU Standard C++ Library v3
Datasets:
  • alog_pcfb - Studies of alogliptin for lowering blood glucose concentration in patients with type II diabetes
  • copd - Studies comparing Tiotropium, Aclidinium and Placebo for maintenance treatment of moderate to severe chronic obstructive pulmonary disease
  • diabetes - Studies comparing treatments for type 2 diabetes
  • goutSUA_CFB - Studies of treatments for reducing serum uric acid in patients with gout
  • goutSUA_CFBcomb - Studies of combined treatments for reducing serum uric acid in patients with gout
  • hyalarthritis - Studies comparing hyaluronan (HA)–based viscosupplements for osteoarthritis
  • obesityBW_CFB - Studies of treatments for reducing body weight in patients with obesity
  • osteopain - Studies of pain relief medications for osteoarthritis

On CRAN:

Conda:

jagscpp

3.82 score 1 stars 22 scripts 323 downloads 36 exports 57 dependencies

Last updated from:13db248bba. Checks:4 OK. Indexed: no.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK172
source / vignettesOK319
linux-release-x86_64OK166
wasm-releaseOK133

Exports:%>%add_indexbinplotcumrankdefault.priorsdevplotfitplotgensplineget.closest.timeget.earliest.timeget.latest.timeget.priorget.relativegetjagsdatagetnmadatainconsistency.loopsmb.comparisonsmb.make.contrastmb.networkmb.nodesplitmb.nodesplit.comparisonsmb.runmb.updatemb.writenma.runrankref.synthtemaxtfpolytimeplottitptloglintpolytsplinetuserwrite.ref.synth

Dependencies:abindbackportsbootcheckmateclicodacpp11crayondistributionaldplyrevaluatefarvergenericsggdistggplot2gluegridExtragtablehighrigraphisobandknitrlabelinglatticelifecyclelsplinemagrittrMatrixnumDerivpillarpkgconfigplyrpngquadprogR2jagsR2WinBUGSR6rbibutilsRColorBrewerRcppRdpackreshape2rjagsrlangS7scalesstringistringrtibbletidyselectutf8vctrsviridisLitewithrxfunyamlzoo

Calculating model predictions

Hugo Pedder

Rendered frompredictions-5.Rmdusingknitr::rmarkdownon May 21 2026.

Last update: 2025-01-29
Started: 2023-10-14

Checking for consistency

Hugo Pedder

Rendered fromconsistencychecking-3.Rmdusingknitr::rmarkdownon May 21 2026.

Last update: 2025-01-29
Started: 2023-10-14

Exploring the data

Hugo Pedder

Rendered fromdataexploration-1.Rmdusingknitr::rmarkdownon May 21 2026.

Last update: 2025-01-29
Started: 2023-10-14

MBNMAtime: Package Overview

Hugo Pedder

Rendered frommbnmatime-overview.Rmdusingknitr::rmarkdownon May 21 2026.

Last update: 2023-10-14
Started: 2023-10-14

Outputs: Relative effects, forest plots and rankings

Hugo Pedder

Rendered fromoutputs-4.Rmdusingknitr::rmarkdownon May 21 2026.

Last update: 2025-01-29
Started: 2023-10-14

Perform a time-course Model-Based Network Meta-Analysis (MBNMA)

Hugo Pedder

Rendered fromrunmbnmatime-2.Rmdusingknitr::rmarkdownon May 21 2026.

Last update: 2025-01-29
Started: 2023-10-14

Readme and manuals

Help Manual

Help pageTopics
Add follow-up time and arm indices to a datasetadd_index
Studies of alogliptin for lowering blood glucose concentration in patients with type II diabetesalog_pcfb
Plot relative effects from NMAs performed at multiple time-binsbinplot
Studies comparing Tiotropium, Aclidinium and Placebo for maintenance treatment of moderate to severe chronic obstructive pulmonary diseasecopd
Plot cumulative ranking curves from MBNMA modelscumrank
Sets default priors for JAGS model codedefault.priors
Plot deviance contributions from an MBNMA modeldevplot
Studies comparing treatments for type 2 diabetesdiabetes
Plot fitted values from MBNMA modelfitplot
Automatically generate parameters to save for a time-course MBNMA modelgen.parameters.to.save
Get large vector of distinct colours using Rcolorbrewergenmaxcols
Generates spline basis matrices for fitting to time-course functiongenspline
Create a dataset with a single time point from each study closest to specified timeget.closest.time
Create a dataset with the earliest time point onlyget.earliest.time
Create a dataset with the latest time point onlyget.latest.time
Get MBNMA model valuesget.model.vals
Get current priors from JAGS model codeget.prior
Calculates relative effects/mean differences at a particular time-pointget.relative
Prepares data for JAGSgetjagsdata
Prepares NMA data for JAGSgetnmadata
Studies of treatments for reducing serum uric acid in patients with goutgoutSUA_CFB
Studies of combined treatments for reducing serum uric acid in patients with goutgoutSUA_CFBcomb
Studies comparing hyaluronan (HA)–based viscosupplements for osteoarthritishyalarthritis
Identify comparisons in loops that fulfil criteria for node-splittinginconsistency.loops
Identify unique comparisons within a network (identical to MBNMAdose)mb.comparisons
Convert arm-based MBNMA data to contrast datamb.make.contrast
Identify comparisons in time-course MBNMA datasets that fulfil criteria for node-splittingmb.nodesplit.comparisons
Run MBNMA time-course modelsmb.run
Update MBNMA to obtain deviance contributions or fitted valuesmb.update
Validates that a dataset fulfils requirements for MBNMAmb.validate.data
Write MBNMA time-course models JAGS codemb.write
Run an NMA modelnma.run
Studies of treatments for reducing body weight in patients with obesityobesityBW_CFB
Studies of pain relief medications for osteoarthritisosteopain
Calculate plugin pD from a JAGS model with univariate likelihood for studies with repeated measurementspDcalc
Create an 'mb.network' objectmb.network plot.mb.network
Plots predicted responses from a time-course MBNMA modelplot.mb.predict
Plot histograms of rankings from MBNMA modelsplot.mb.rank
Forest plot for results from time-course MBNMA modelsplot.mbnma
Perform node-splitting on a MBNMA time-course networkmb.nodesplit plot.nodesplit
Predict effects over time in a given population based on MBNMA time-course modelspredict.mbnma
Print mb.network information to the consoleprint.mb.network
Print summary information from an mb.predict objectprint.mb.predict
Prints a summary of rankings for each parameterprint.mb.rank
Prints basic results from a node-split to the consoleprint.nodesplit
Print posterior medians (95% credible intervals) for table of relative effects/mean differences between treatments/classesprint.relative.array
Calculate position of label with respect to vertex location within a circleradian.rescale
Set rank as a methodrank
Rank predictions at a specific time pointrank.mb.predict
Rank parameters from a time-course MBNMArank.mbnma
Calculates ranking probabilities for AUC from a time-course MBNMArankauc
Identify unique comparisons relative to study reference treatment within a networkref.comparisons
Synthesise single arm studies with repeated observations of the same treatment over timeref.synth
Checks the validity of ref.resp if given as data frameref.validate
Removes any loops from MBNMA model JAGS code that do not contain any expressionsremove.loops
Replace original priors in an MBNMA model with new priorsreplace.prior
Print summary mb.network information to the consolesummary.mb.network
Prints summary of mb.predict objectsummary.mb.predict
Print summary MBNMA results to the consolesummary.mbnma
Takes node-split results and produces summary data framesummary.nodesplit
Emax time-course functiontemax
Fractional polynomial time-course functiontfpoly
Plot raw responses over time by treatment or classtimeplot
Integrated Two-Component Prediction (ITP) functiontitp
Log-linear (exponential) time-course functiontloglin
Polynomial time-course functiontpoly
Spline time-course functionstspline
User-defined time-course functiontuser
Adds sections of JAGS code for an MBNMA model that correspond to beta parameterswrite.beta
Checks validity of arguments for mb.writewrite.check
Adds correlation between time-course relative effectswrite.cor
Adds sections of JAGS code for an MBNMA model that correspond to the likelihoodwrite.likelihood
Write the basic JAGS model code for MBNMA to which other lines of model code can be addedwrite.model
Write MBNMA time-course models JAGS code for synthesis of studies investigating reference treatmentwrite.ref.synth
Adds sections of JAGS code for an MBNMA model that correspond to alpha parameterswrite.timecourse