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).
|Author||Pete Philipson [aut, cre] (<https://orcid.org/0000-0001-7846-0208>>), Ines Sousa [aut] (<https://orcid.org/0000-0002-2712-1713>>), Peter J. Diggle [aut] (<https://orcid.org/0000-0003-3521-5020>>), Paula Williamson [aut] (<https://orcid.org/0000-0001-9802-6636>>), Ruwanthi Kolamunnage-Dona [aut] (<https://orcid.org/0000-0003-3886-6208>>), Robin Henderson [aut], Graeme L. Hickey [aut] (<https://orcid.org/0000-0002-4989-0054>>), Maria Sudell [ctb], Medical Research Council [fnd] (Grant numbers: G0400615 and MR/M013227/1)|
|Maintainer||Pete Philipson <[email protected]>|
|License||GPL-3 | file LICENSE|
|Package repository||View on CRAN|
Install the latest version of this package by entering the following in R:
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.