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

---

Documentation for package ‘BJM’ version 0.3.0

• DESCRIPTION file.
• User guides, package vignettes and other documentation.
• Package NEWS.

Help Pages

buildRecurrentEpisodes	Reshape recurrent-event data into gap-time episodes for the backward joint model
calibrationPlot	Plot calibration of dynamic risk predictions
checkBandcountConvergence	Check whether bandcount1/bandcount2/bandcount3 are large enough
cifPlot	Plot cumulative incidence functions
cmtPlot	Plot conditional mean trajectories (CMT)
fitIntervalBJM	Fit the backward joint model to interval-censored event times
imputeLongitudinal	Impute missing longitudinal biomarker values
longitudinalSub	Fit a multivariate longitudinal sub-model
pbc3	Mayo Clinic primary biliary cirrhosis data used as example code
performancePlot	Plot predictive performance across landmark times
plot.longitudinalSub.BJM	Plot a fitted longitudinal sub-model
plot.predictLongitudinal.BJM	Plot predicted biomarker distributions
plot.simulateTrajectory.BJM	Plot simulated futures
plot.survivalSub.BJM	Plot a fitted survival sub-model
poolLongitudinalSub	Pool longitudinal sub-model fits across multiple imputations with Rubin's rules
predictLongitudinal	Dynamic prediction function for future longitudinal outcomes
predictPlot	Plot risk and future biomarker predictions across a horizon sweep
predictRecurrentCount	Predict the number of recurrent events in a window, by rolling episodes forward
predictRisk	Dynamic prediction function for future event risk
riskPlot	Plot predicted risk across a sweep of landmark times
simulateTrajectory	Simulate future trajectories from the backward joint model
spaghettiPlot	Plot individual longitudinal trajectories (spaghetti plot)
survivalSub	Fit the survival sub-model
survivalTrans	Build a survival-time transform basis from cut points