Package: joineRML 0.4.6

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]

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joineRML.pdf |joineRML.html✨
joineRML/json (API)
NEWS

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

Peer review:

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

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:

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

11 exports 30 stars 2.74 score 48 dependencies 1 dependents 1 mentions 132 scripts 1.3k downloads

Last updated 2 years agofrom:e68d68585e. Checks:OK: 1 NOTE: 8. Indexed: yes.

TargetResultDate
Doc / VignettesOKSep 18 2024
R-4.5-win-x86_64NOTESep 18 2024
R-4.5-linux-x86_64NOTESep 18 2024
R-4.4-win-x86_64NOTESep 18 2024
R-4.4-mac-x86_64NOTESep 18 2024
R-4.4-mac-aarch64NOTESep 18 2024
R-4.3-win-x86_64NOTESep 18 2024
R-4.3-mac-x86_64NOTESep 18 2024
R-4.3-mac-aarch64NOTESep 18 2024

Exports:augmentbaseHazbootSEdynLongdynSurvglancemjointplotConvergencesampleDatasimDatatidy

Dependencies:bootclicobscodetoolscolorspacedoParallelfansifarverforeachgenericsggplot2gluegtableisobanditeratorslabelinglatticelifecyclelme4magrittrMASSMatrixMatrixModelsmgcvminqamunsellmvtnormnlmenloptrpillarpkgconfigquantregR6randtoolboxRColorBrewerRcppRcppArmadilloRcppEigenrlangrngWELLscalesSparseMsurvivaltibbleutf8vctrsviridisLitewithr

joineRML

Graeme L. Hickey

Rendered fromjoineRML.Rmdusingknitr::rmarkdownon Sep 18 2024.

Last update: 2023-01-19
Started: 2016-10-20

joineRML and the broom package

Alessandro Gasparini

Rendered fromjoineRML-tidy.Rmdusingknitr::rmarkdownon Sep 18 2024.

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

Technical details of joineRML

Rendered fromtechnical.Rnwusingutils::Sweaveon Sep 18 2024.

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