frailtypack (version 2.7.1)

frailtypack-package: General Frailty models: shared, joint and nested frailty models with prediction

Description

Frailtypack fits several classes of frailty models using a penalized likelihood estimation on the hazard function but also a parametric estimation. 1) A shared frailty model and Cox proportional hazard model. Clustered and recurrent survival times can be studied. 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 are allowed. In each model, the random effects have a gamma distribution, but you can switch to a log-normal in shared and joint. Now, you can also consider time-varying effect covariates in Cox, shared and joint models. The package includes concordance measures for Cox proportional hazards models and for shared frailty models.

Details

ll{ Package: frailtypack Type: Package Version: 2.7 Date: 2014-07-15 License: GPL (>= 2.0) LazyLoad: no }

References

V. Rondeau, Y. Mazroui and J. R. Gonzalez (2012). Frailtypack: An R package for the analysis of correlated survival data with frailty models using penalized likelihood estimation or parametric estimation. Journal of Statistical Software 47, 1-28. V. Rondeau and J. R. Gonzalez (2005). Frailtypack: A computer program for the analysis of correlated failure time data using penalized likelihood estimation. Computer Methods and Programs in Biomedicine 80, 2, 154-164. V. Rondeau, S. Michiels, B. Liquet, and J. P. Pignon (2008). Investigating trial and treatment heterogeneity in an individual patient data meta-analysis of survival data by mean of the maximum penalized likelihood approach. Statistics in Medecine, 27, 1894-1910. V. Rondeau, S. Mathoulin-Pellissier, H. Jacqmin-Gadda, V. Brouste, P. Soubeyran (2007). Joint frailty models for recurring events and death using maximum penalized likelihood estimation:application on cancer events. Biostatistics, 8, 4, 708-721. V. Rondeau, D. Commenges, and P. Joly (2003). Maximum penalized likelihood estimation in a gamma-frailty model. Lifetime Data Analysis 9, 139-153. D. Marquardt (1963). An algorithm for least-squares estimation of nonlinear parameters. SIAM Journal of Applied Mathematics, 431-441. V. Rondeau, L. Filleul, P. Joly (2006). Nested frailty models using maximum penalized likelihood estimation. Statistics in Medecine, 25, 4036-4052.

Examples

###--- Additive model with 1 covariate ---###

data(dataAdditive)
modAdd <- additivePenal(Surv(t1,t2,event)~
cluster(group)+var1+slope(var1),
correlation=TRUE,data=dataAdditive,
n.knots=8,kappa=10000,hazard="Splines")

###--- Joint model (recurrent and terminal events) with 2 covariates ---###

data(readmission)
modJoint.gap <- frailtyPenal(Surv(time,event)~
cluster(id)+sex+dukes+charlson+terminal(death),
formula.terminalEvent=~sex+dukes+charlson,
data=readmission,n.knots=10,kappa=c(100,100),
recurrentAG=FALSE,hazard="Splines")

###--- Nested model (or hierarchical model) with 2 covariates ---###

data(dataNested)
modClu <- frailtyPenal(Surv(t1,t2,event)~
cluster(group)+subcluster(subgroup)+cov1+cov2,
data=dataNested,n.knots=8,kappa=50000,hazard="Splines")

###--- Semiparametric Shared model ---###

data(readmission)
sha.sp <- frailtyPenal(Surv(t.start,t.stop,event)~
sex+dukes+charlson+cluster(id),data=readmission,
n.knots=6,kappa=5000,recurrentAG=TRUE,
cross.validation=TRUE,hazard="Splines")

###--- Parametric Shared model ---###

data(readmission)
sha.p <- frailtyPenal(Surv(t.start,t.stop,event)~
cluster(id)+sex+dukes+charlson,
data=readmission,recurrentAG=TRUE,
hazard="Piecewise-per",nb.int=6)