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 13 articles , 1 standard article )

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  1. Groll, Andreas; Tutz, Gerhard: Variable selection in discrete survival models including heterogeneity (2017)
  2. John V. Monaco, Malka Gorfine, Li Hsu: General Semiparametric Shared Frailty Model Estimation and Simulation with frailtySurv (2017) arXiv
  3. Commenges, Daniel; Jacqmin-Gadda, Hélène: Dynamical biostatistical models (2016)
  4. Mazroui, Yassin; Mauguen, Audrey; Mathoulin-Pélissier, Simone; MacGrogan, Gaetan; Brouste, Véronique; Rondeau, Virginie: Time-varying coefficients in a multivariate frailty model: application to breast cancer recurrences of several types and death (2016)
  5. Wen, Chi-Chung; Chen, Yi-Hau: Joint analysis of current count and current status data (2016)
  6. Huang, C.-Y.; Qin, J.; Wang, M.-C.: Semiparametric analysis for recurrent event data with time-dependent covariates and informative censoring (2010)
  7. Rondeau, V.: Statistical models for recurrent events and death: application to cancer events (2010)
  8. Song, Rui; Cai, Jianwen: Joint covariate-adjusted score test statistics for recurrent events and a terminal event (2010)
  9. Du, Pang: Nonparametric modeling of the gap time in recurrent event data (2009)
  10. He, Xin; Tong, Xingwei; Sun, Jianguo: Semiparametric analysis of panel count data with correlated observation and follow-up times (2009)
  11. Liu, Lei; Huang, Xuelin; O’Quigley, John: Analysis of longitudinal data in the presence of informative observational times and a dependent terminal event, with application to medical cost data (2008)
  12. Rondeau, Virginie; Mathoulin-Pelissier, Simone; Jacqmin-Gadda, Hélène; Brouste, Véronique; Soubeyran, Pierre: Joint frailty models for recurring events and death using maximum penalized likelihood estimation: application on cancer events (2007)
  13. Rondeau, Virginie; Gonzalez, Juan R.: Frailtypack: A computer program for the analysis of correlated failure time data using penalized likelihood estimation. (2005) ioport