JM: an R package for the joint modelling of longitudinal and time-to-event data. 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.

This software is also peer reviewed by journal JSS.

References in zbMATH (referenced in 14 articles )

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  1. Desmée, Solène; Mentré, France; Veyrat-Follet, Christine; Sébastien, Bernard; Guedj, Jérémie: Using the SAEM algorithm for mechanistic joint models characterizing the relationship between nonlinear PSA kinetics and survival in prostate cancer patients (2017)
  2. Maziarz, Marlena; Heagerty, Patrick; Cai, Tianxi; Zheng, Yingye: On longitudinal prediction with time-to-event outcome: comparison of modeling options (2017)
  3. Khan, Shahedul A.; Khosa, Saima K.: Generalized log-logistic proportional hazard model with applications in survival analysis (2016)
  4. Bartolucci, Francesco; Farcomeni, Alessio: A discrete time event-history approach to informative drop-out in mixed latent Markov models with covariates (2015)
  5. Dimitris Rizopoulos: The R Package JMbayes for Fitting Joint Models for Longitudinal and Time-to-Event Data using MCMC (2014) arXiv
  6. Viviani, Sara; Alfó, Marco; Rizopoulos, Dimitris: Generalized linear mixed joint model for longitudinal and survival outcomes (2014)
  7. Efendi, Achmad; Molenberghs, Geert; Njagi, Edmund Njeru; Dendale, Paul: A joint model for longitudinal continuous and time-to-event outcomes with direct marginal interpretation (2013)
  8. Jones, Geoff: Book review of: D. Rizopoulos, Joint models for longitudinal and time-to-event data. With applications in R (2013)
  9. Commenges, Daniel; Liquet, Benoit; Proust-Lima, Cécile: Choice of prognostic estimators in joint models by estimating differences of expected conditional Kullback-Leibler risks (2012)
  10. Rizopoulos, Dimitris: Fast fitting of joint models for longitudinal and event time data using a pseudo-adaptive Gaussian quadrature rule (2012)
  11. Rizopoulos, Dimitris: Joint models for longitudinal and time-to-event data. With applications in R (2012)
  12. Lee, Terry C.K.; Zeng, Leilei; Thompson, Darby J.S.; Dean, C.B.: Comparison of imputation methods for interval censored time-to-event data in joint modelling of tree growth and mortality (2011)
  13. Rizopoulos, Dimitris: Dynamic predictions and prospective accuracy in joint models for longitudinal and time-to-event data (2011)
  14. Sweeting, Michael J.; Thompson, Simon G.: Joint modelling of longitudinal and time-to-event data with application to predicting abdominal aortic aneurysm growth and rupture (2011)