Joint Modelling of Multivariate Longitudinal Data and Time-to-Event Outcomes
The baseline hazard estimate of an mjoint object
Standard errors via bootstrap for an mjoint object
Confidence intervals for model parameters of an mjoint object
Dynamic predictions for the longitudinal data sub-model
Dynamic predictions for the time-to-event data sub-model
Extract mjoint fitted values
Fitted mjoint object
Fit a joint model to time-to-event data and multivariate longitudinal ...
Tidying methods for joint models for time-to-event data and multivaria...
Summary of an mjoint object
Extract fixed effects estimates from an mjoint object
Extract model formulae from an mjoint object
Extract variance-covariance matrix of random effects from an mjointo...
joineRML
Extract log-likelihood from an mjoint object
Plot a dynLong object
Plot a dynSurv object
Plot diagnostics from an mjoint object
Plot a ranef.mjoint object
Plot convergence time series for parameter vectors from an mjointobj...
Extract random effects estimates from an mjoint object
Objects exported from other packages
Extract mjoint residuals
Sample from an mjoint object
Extract residual standard deviation(s) from an mjoint object
Simulate data from a joint model
Extract an approximate variance-covariance matrix of estimated paramet...
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).
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