JM: An R package for the joint modelling of longitudinal and time-to-event data
By Dimitris Rizopoulos. 2010
542 citations recorded by the source corpus.
In longitudinal studies measurements are often collected on different types of outcomes for each subject. These may include several longitudinally measured responses (such as blood values relevant to the medical condition under study) and the time at which an event of particular interest occurs (e.g., death, development of a disease or dropout from the study). These outcomes are often separately analyzed; however, in many instances, a joint modeling approach is either required or may produce a better insight into the mechanisms that underlie the phenomenon under study. In this paper we present the R package JM that fits joint models for longitudinal and time-to-event data.
Subfields: Management Science and Operations Research, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/1a55762452070dc9bc18cd8032389e3772e149c1
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In Management Science and Operations Research
- Suchi Saria
- Hossein Soleimani
- James Hensman
- Dimitris Rizopoulos
- Brooke E. Magnus
- Christel Koop
- Chris Hanretty
- Felix D. Schönbrodt
In Statistics and Probability
- Suchi Saria
- Hossein Soleimani
- James Hensman
- Dimitris Rizopoulos
- Brooke E. Magnus
- Thang D. Bui
- Richard E. Turner
- Mihaela van der Schaar
Citations in the corpus
- Dynamic Predictions and Prospective Accuracy in Joint Models for Longitudinal and Time‐to‐Event Data (2011, methodology): “All of our proposals have been implemented in R with the freely available package JM (Rizopoulos, 2010).”
- Scalable Joint Models for Reliable Uncertainty-Aware Event Prediction (2017, methodology): “Similar ideas for incorporating signal histories have also been explored by Rizopoulos et al. [14].”