Package: joineRML 0.4.8

joineRML: Joint Modelling of Multivariate Longitudinal Data and Time-to-Event Outcomes

Fits the joint model proposed by Henderson and colleagues (2000) <doi:10.1093/biostatistics/1.4.465>, but extended to the case of multiple continuous longitudinal measures. The time-to-event data is modelled using a Cox proportional hazards regression model with time-varying covariates. The multiple longitudinal outcomes are modelled using a multivariate version of the Laird and Ware linear mixed model. The association is captured by a multivariate latent Gaussian process. The model is estimated using a Monte Carlo Expectation Maximization algorithm. This project was funded by the Medical Research Council (Grant number MR/M013227/1).

Authors:Graeme L. Hickey [cre, aut], Pete Philipson [aut], Andrea Jorgensen [ctb], Ruwanthi Kolamunnage-Dona [aut], Paula Williamson [ctb], Dimitris Rizopoulos [ctb, dtc], Alessandro Gasparini [aut], Medical Research Council [fnd]

joineRML_0.4.8.tar.gz
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joineRML_0.4.8.tgz(r-4.6-x86_64)joineRML_0.4.8.tgz(r-4.6-arm64)joineRML_0.4.8.tgz(r-4.5-x86_64)joineRML_0.4.8.tgz(r-4.5-arm64)
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joineRML_0.4.8.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
joineRML/json (API)

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

Bug tracker:https://github.com/graemeleehickey/joinerml/issues

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

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
  • openmp– GCC OpenMP (GOMP) support library
Datasets:
  • epileptic.qol - Quality of life data following epilepsy drug treatment
  • heart.valve - Aortic valve replacement surgery data
  • pbc2 - Mayo Clinic primary biliary cirrhosis data
  • renal - Renal transplantation data

On CRAN:

Conda:

armadillobiostatisticsclinical-trialscoxdynamicjoint-modelslongitudinal-datamultivariate-analysismultivariate-datamultivariate-longitudinal-datapredictionrcppregression-modelsstatisticssurvivalopenblascppopenmp

9.04 score 34 stars 2 packages 180 scripts 10k downloads 1 mentions 11 exports 49 dependencies

Last updated from:451eadcc56. Checks:13 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64OK276
linux-devel-x86_64OK278
source / vignettesOK406
linux-release-arm64OK241
linux-release-x86_64OK270
macos-release-arm64OK161
macos-release-x86_64OK417
macos-oldrel-arm64OK286
macos-oldrel-x86_64OK322
windows-develOK328
windows-releaseOK236
windows-oldrelOK282
wasm-releaseOK160

Exports:augmentbaseHazbootSEdynLongdynSurvglancemjointplotConvergencesampleDatasimDatatidy

Dependencies:bootclicobscodetoolscpp11doParallelfarverforeachgenericsggplot2gluegtableisobanditeratorslabelinglatticelifecyclelme4magrittrMASSMatrixMatrixModelsminqamvtnormnlmenloptrpillarpkgconfigquantregR6randtoolboxrbibutilsRColorBrewerRcppRcppArmadilloRcppEigenRdpackreformulasrlangrngWELLS7scalesSparseMsurvivaltibbleutf8vctrsviridisLitewithr

joineRML
Introduction | Heart valve data | Data | Model fitting | Post-fit analysis | Bootstrap standard errors | Univariate joint models: joineRML versus joineR
Rendered fromjoineRML.Rmdusingknitr::rmarkdown

Last update: 2025-02-03
Started: 2016-10-20

joineRML and the broom package
Introduction | Example | tidy method | augment method | glance method | Additional examples
Rendered fromjoineRML-tidy.Rmdusingknitr::rmarkdown

Last update: 2025-02-03
Started: 2020-03-14

Technical details of joineRML
Model and notation | Estimation | Simulation | Appendix: Score equations
Rendered fromtechnical.Rnwusingutils::Sweave

Last update: 2017-05-31
Started: 2017-04-28

Readme and manuals

Help Manual

Help pageTopics
The baseline hazard estimate of an 'mjoint' objectbaseHaz
Standard errors via bootstrap for an 'mjoint' objectbootSE
Confidence intervals for model parameters of an 'mjoint' objectconfint.mjoint
Dynamic predictions for the longitudinal data sub-modeldynLong
Dynamic predictions for the time-to-event data sub-modeldynSurv
Quality of life data following epilepsy drug treatmentepileptic.qol
Extract 'mjoint' fitted valuesfitted.mjoint
Extract fixed effects estimates from an 'mjoint' objectfixef.mjoint
Extract model formulae from an 'mjoint' objectformula.mjoint
Extract variance-covariance matrix of random effects from an 'mjoint' objectgetVarCov.mjoint
Aortic valve replacement surgery dataheart.valve
joineRMLjoineRML
Extract log-likelihood from an 'mjoint' objectlogLik.mjoint
Fit a joint model to time-to-event data and multivariate longitudinal datamjoint
Tidying methods for joint models for time-to-event data and multivariate longitudinal dataaugment.mjoint glance.mjoint mjoint_tidiers tidy.mjoint
Fitted 'mjoint' objectmjoint.object
Mayo Clinic primary biliary cirrhosis datapbc2
Plot a 'dynLong' objectplot.dynLong
Plot a 'dynSurv' objectplot.dynSurv
Plot diagnostics from an 'mjoint' objectplot.mjoint
Plot a 'ranef.mjoint' objectplot.ranef.mjoint
Plot convergence time series for parameter vectors from an 'mjoint' objectplotConvergence
Extract random effects estimates from an 'mjoint' objectranef.mjoint
Renal transplantation datarenal
Extract 'mjoint' residualsresiduals.mjoint
Sample from an 'mjoint' objectsampleData
Extract residual standard deviation(s) from an 'mjoint' objectsigma.mjoint
Simulate data from a joint modelsimData
Summary of an 'mjoint' objectsummary.mjoint
Extract an approximate variance-covariance matrix of estimated parameters from an 'mjoint' objectvcov.mjoint