frailtypack: General Frailty models using a semi_parametric penalized likelihood estimation or a parametric estimation , Frailtypack now fits several classes of frailty models using a penalized likelihood estimation on the hazard function but also a parametric estimation. 1) A shared gamma frailty model and Cox proportional hazard model. Clustered and recurrent survival times can be studied (the Andersen-Gill(1982) approach has been implemented for recurrent events). An automatic choice of the smoothing parameter is possible using an approximated cross-validation procedure. 2) Additive frailty models for proportional hazard models with two correlated random effects (intercept random effect with random slope). 3) Nested frailty models for hierarchically clustered data (with 2 levels of clustering) by including two iid gamma random effects. 4) Joint frailty models in the context of joint modelling for recurrent events with terminal event for clustered data or not. Prediction values are available. Left truncated (not for Joint model), right-censored data, interval-censored data (only for Cox proportional hazard and shared frailty model) and strata (max=2) are allowed. The package includes concordance measures for Cox proportional hazards models and for shared frailty models. (Source:

References in zbMATH (referenced in 43 articles , 2 standard articles )

Showing results 1 to 20 of 43.
Sorted by year (citations)

1 2 3 next

  1. Zhang, Zhongwen; Wang, Xiaoguang; Peng, Yingwei: An additive hazards frailty model with semi-varying coefficients (2022)
  2. Hadrien Charvat, Aurelien Belot: mexhaz: An R Package for Fitting Flexible Hazard-Based Regression Models for Overall and Excess Mortality with a Random Effect (2021) not zbMATH
  3. Sy Han Chiou, Gongjun Xu, Jun Yan, Chiung-Yu Huang: Regression Modeling for Recurrent Events Using R Package reReg (2021) arXiv
  4. Cong Xu, Pantelis Z. Hadjipantelis, Jane-Ling Wang: Semi-Parametric Joint Modeling of Survival and Longitudinal Data: The R Package JSM (2020) not zbMATH
  5. 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
  6. Andersen, Per Kragh; Angst, Jules; Ravn, Henrik: Modeling marginal features in studies of recurrent events in the presence of a terminal event (2019)
  7. Brown, Paul M.; Ezekowitz, Justin A.: Frailty modelling for multitype recurrent events in clinical trials (2019)
  8. Golzy, Mojgan; Carter, Randy L.: Generalized frailty models for analysis of recurrent events (2019)
  9. Kim, Gwangsu: Posterior consistency in frailty models and simulation studies to test the presence of random effects (2019)
  10. Park, Kayoung; Qiu, Peihua: Comparing crossing hazard rate functions by joint modelling of survival and longitudinal data (2019)
  11. Theodor Balan; Hein Putter: frailtyEM: An R Package for Estimating Semiparametric Shared Frailty Models (2019) not zbMATH
  12. Danilo Alvares, Sebastien Haneuse, Catherine Lee, Kyu Ha Lee: SemiCompRisks: An R Package for Independent and Cluster-Correlated Analyses of Semi-Competing Risks Data (2018) arXiv
  13. Han, Miao; Sun, Liuquan; Liu, Yutao; Zhu, Jun: Joint analysis of recurrent event data with additive-multiplicative hazards model for the terminal event time (2018)
  14. John Monaco; Malka Gorfine; Li Hsu: General Semiparametric Shared Frailty Model: Estimation and Simulation with frailtySurv (2018) not zbMATH
  15. Peter Calhoun; Xiaogang Su;Martha Nunn; Juanjuan Fan: Constructing Multivariate Survival Trees: The MST Package for R (2018) not zbMATH
  16. 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
  17. Groll, Andreas; Tutz, Gerhard: Variable selection in discrete survival models including heterogeneity (2017)
  18. Ha, Il Do; Jeong, Jong-Hyeon; Lee, Youngjo: Statistical modelling of survival data with random effects. H-likelihood approach (2017)
  19. John V. Monaco, Malka Gorfine, Li Hsu: General Semiparametric Shared Frailty Model Estimation and Simulation with frailtySurv (2017) arXiv
  20. Lee, Youngjo; Ronnegård, Lars; Noh, Maengseok: Data analysis using hierarchical generalized linear models with R (2017)

1 2 3 next