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

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Documentation for package ‘BJM’ version 0.2.0

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

Help Pages

checkBandcountConvergence	Check whether bandcount1/bandcount2/bandcount3 are large enough
cmtPlot	Plot conditional mean trajectories (CMT)
dynamicPrediction	Dynamic prediction function
dynamicPredictionBio	Dynamic prediction function for future biomarker
longitudinalSub	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.
pbc3	Mayo Clinic primary biliary cirrhosis data used as example code
predictPlot	Plot of risk and future biomarker with density using dynamic prediction
print.dynamicPrediction.BJM	Print method for 'dynamicPrediction.BJM' objects
print.dynamicPredictionBio.BJM	Print method for 'dynamicPredictionBio.BJM' objects
print.longitudinalSub.BJM	Print method for 'longitudinalSub.BJM' objects
print.survivalSub.BJM	Print method for 'survivalSub.BJM' objects
printBJM	Print both sub-models of a fitted backward joint model
riskPlot	Plot of risk using dynamic prediction
summary.dynamicPrediction.BJM	Summary method for 'dynamicPrediction.BJM' objects
summary.dynamicPredictionBio.BJM	Summary method for 'dynamicPredictionBio.BJM' objects
summary.longitudinalSub.BJM	Summary method for 'longitudinalSub.BJM' objects
summary.survivalSub.BJM	Summary method for 'survivalSub.BJM' objects
survivalSub	Fitting survival sub-model
survivalTrans	Build a survival-time transform basis from cut points