Package: BJM 0.1.0

Wenhao Li

BJM: Backward Joint Model for the Dynamic Prediction of Both Time-to-Event and Longitudinal Outcomes

Provides tools to fit joint models of multivariate longitudinal data and time-to-event data for dynamic prediction. It allows the joint prediction of both future time-to-event outcomes and future longitudinal outcomes conditional on survival. The models accommodate irregularly measured longitudinal data and competing risks outcomes. The use of the backward joint model enables fast and efficient computation, especially for applications with large sample sizes and many longitudinal variables.

Authors:Wenhao Li [aut, cre], Liang Li [aut]

BJM_0.1.0.tar.gz
BJM_0.1.0.tar.gz(r-4.7-any)BJM_0.1.0.tar.gz(r-4.6-any)
BJM_0.1.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
BJM/json (API)

# Install 'BJM' in R:
install.packages('BJM', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))
Datasets:
  • pbc2 - Mayo Clinic primary biliary cirrhosis data
  • pbc3 - Mayo Clinic primary biliary cirrhosis data used as example code

On CRAN:

Conda:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

1.00 score 12 exports 22 dependencies

Last updated from:d5c44f3c6c. Checks:4 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK141
source / vignettesOK167
linux-release-x86_64OK135
wasm-releaseOK101

Exports:cmtPlotdynamicPredictiondynamicPredictionBiolongitudinalSubpredictPlotprint_BJMprint_dynamicPredictionprint_dynamicPredictionBioprint_longitudinalSubprint_survivalSubriskPlotsurvivalSub

Dependencies:clicpp11farverggplot2gluegtableisobandlabelinglatticelifecycleMatrixmvtnormnlmeR6RColorBrewerrlangS7scalessurvivalvctrsviridisLitewithr

Readme and manuals

Help Manual

Help pageTopics
Plot conditional mean trajectories (CMT)cmtPlot
Dynamic prediction functiondynamicPrediction
Dynamic prediction function for future biomarkerdynamicPredictionBio
The process involves estimating parameters for a multivariate linear mixed-effects model, which simultaneously analyzes multiple dependent variables that may be correlated. This approach incorporates both fixed effects, which are consistent across the population, and random effects, accounting for variations within groups or subjects. By fitting this model, one can assess the influence of predictor variables on several longitudinal outcomes while considering the inherent variability in the data due to random effects.longitudinalSub
Mayo Clinic primary biliary cirrhosis datapbc2
Mayo Clinic primary biliary cirrhosis data used as example codepbc3
Plot of risk and future biomarker with density using dynamic predictionpredictPlot
Combined print summary for a fitted BJMprint_BJM
Print method for 'dynamicPrediction.BJM' objectsprint.dynamicPrediction.BJM print_dynamicPrediction
Print method for 'dynamicPredictionBio.BJM' objectsprint.dynamicPredictionBio.BJM print_dynamicPredictionBio
Print method for 'longitudinalSub.BJM' objectsprint.longitudinalSub.BJM print_longitudinalSub
Print method for 'survivalSub.BJM' objectsprint.survivalSub.BJM print_survivalSub
Plot of risk using dynamic predictionriskPlot
Summary method for 'dynamicPrediction.BJM' objectssummary.dynamicPrediction.BJM
Summary method for 'dynamicPredictionBio.BJM' objectssummary.dynamicPredictionBio.BJM
Summary method for 'longitudinalSub.BJM' objectssummary.longitudinalSub.BJM
Summary method for 'survivalSub.BJM' objectssummary.survivalSub.BJM
Fitting survival sub-modelsurvivalSub