Joint Modelling of Repeated Measurements and Time-to-Event Data
joineR
Fitted joint object
Fit joint model for survival and longitudinal data measured with error
Creates an object of class jointdata
Joint plot of longitudinal and survival data
Standard errors via bootstrap for a joint model fit
Add lines to an existing jointdata plot
Plot longitudinal data
Plots the empirical variogram for longitudinal data
Add points to an existing jointdata plot
Sample from a jointdata x
Simulate data from a joint model
Subsetting object of class jointdata
Summarise a random effects joint model fit
Summarise a jointdata object
Summary of a balanced longitudinal data set
Transform data to the longitudinal balanced format
Transform data to the longitudinal unbalanced format
Extracts the unique non-time dependent variables per patient, from an ...
Empirical variogram for longitudinal data
Analysis of repeated measurements and time-to-event data via random effects joint models. Fits the joint models proposed by Henderson and colleagues <doi:10.1093/biostatistics/1.4.465> (single event time) and by Williamson and colleagues (2008) <doi:10.1002/sim.3451> (competing risks events time) to a single continuous repeated measure. The time-to-event data is modelled using a (cause-specific) Cox proportional hazards regression model with time-varying covariates. The longitudinal outcome is modelled using a linear mixed effects model. The association is captured by a latent Gaussian process. The model is estimated using am Expectation Maximization algorithm. Some plotting functions and the variogram are also included. This project is funded by the Medical Research Council (Grant numbers G0400615 and MR/M013227/1).
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