JMbayes

The R Package JMbayes for Fitting Joint Models for Longitudinal and Time-to-Event Data using MCMC. Joint models for longitudinal and time-to-event data constitute an attractive modeling framework that has received a lot of interest in the recent years. This paper presents the capabilities of the R package JMbayes for fitting these models under a Bayesian approach using Markon chain Monte Carlo algorithms. JMbayes can fit a wide range of joint models, including among others joint models for continuous and categorical longitudinal responses, and provides several options for modeling the association structure between the two outcomes. In addition, this package can be used to derive dynamic predictions for both outcomes, and offers several tools to validate these predictions in terms of discrimination and calibration. All these features are illustrated using a real data example on patients with primary biliary cirrhosis.


References in zbMATH (referenced in 12 articles )

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  1. Thomas, Abin; Vishwakarma, Gajendra K.; Bhattacharjee, Atanu: Joint modeling of longitudinal and time-to-event data on multivariate protein biomarkers (2021)
  2. Chenguang Wang, Elizabeth Colantuoni, Andrew Leroux, Daniel O. Scharfstein: idem: An R Package for Inferences in Clinical Trials with Death and Missingness (2020) not zbMATH
  3. Cong Xu, Pantelis Z. Hadjipantelis, Jane-Ling Wang: Semi-Parametric Joint Modeling of Survival and Longitudinal Data: The R Package JSM (2020) not zbMATH
  4. Emma C. Martin, Alessandro Gasparini, Michael J. Crowther: merlin: An R package for Mixed Effects Regression for Linear, Nonlinear and User-defined models (2020) arXiv
  5. Mauff, Katya; Steyerberg, Ewout; Kardys, Isabella; Boersma, Eric; Rizopoulos, Dimitris: Joint models with multiple longitudinal outcomes and a time-to-event outcome: a corrected two-stage approach (2020)
  6. Thi Thu Pham, Huong; Pham, Hoa; Nur, Darfiana: A Bayesian inference for the penalized spline joint models of longitudinal and time-to-event data: a prior sensitivity analysis (2020)
  7. Alberto Garcia-Hernandez; Dimitris Rizopoulos: %JM: A SAS Macro to Fit Jointly Generalized Mixed Models for Longitudinal Data and Time-to-Event Responses (2018) not zbMATH
  8. Agnieszka Król; Audrey Mauguen; Yassin Mazroui; Alexandre Laurent; Stefan Michiels; Virginie Rondeau: Tutorial in Joint Modeling and Prediction: A Statistical Software for Correlated Longitudinal Outcomes, Recurrent Events and a Terminal Event (2017) not zbMATH
  9. Piulachs, Xavier; Alemany, Ramon; Guillén, Montserrat; Rizopoulos, Dimitris: Joint models for longitudinal counts and left-truncated time-to event data with applications to health insurance (2017)
  10. Danjie Zhang; Ming-Hui Chen; Joseph Ibrahim; Mark Boye; Wei Shen: JMFit: A SAS Macro for Joint Models of Longitudinal and Survival Data (2016) not zbMATH
  11. Serrat, Carles; Rué, Montserrat; Armero, Carmen; Piulachs, Xavier; Perpiñán, Hèctor; Forte, Anabel; Páez, Álvaro; Gómez, Guadalupe: Frequentist and Bayesian approaches for a joint model for prostate cancer risk and longitudinal prostate-specific antigen data (2015)
  12. Dimitris Rizopoulos: The R Package JMbayes for Fitting Joint Models for Longitudinal and Time-to-Event Data using MCMC (2014) arXiv