Description Details Author(s) References Examples

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. A joint frailty model for two semi-competing risks for clustered data is also proposed. 5) Joint General frailty models in the context of a joint modelling for recurrent events with terminal event data with two independent frailty terms. 6) Joint Nested frailty models in the context of joint modelling for recurrent events with terminal event, for hierarchically clustered data (with two levels of clustering) by including two iid gamma random effects. 7) Multivariate joint frailty models for two types of recurrent events and a terminal event. 8) Joint models for longitudinal data and a terminal event. 9) Trivariate joint models for longitudinal data, recurrent events and a terminal event. Prediction values are available. Left truncated (not for the joint models), 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 the gamma or normal distribution. Now, you can also consider time-varying effect covariates in Cox, shared and joint frailty models. The package includes concordance measures for Cox proportional hazards models and for shared frailty models. 10) Joint frailty models for the validation of surrogate endpoints in multiple randomized clinical trials with failure-time endpoints. This model includes a shared individual-level random effect, a shared trial random-effct associated with the hazard risks and a correlated random effects-by-trial interaction.

Package: | frailtypack |

Type: | Package |

Version: | 3.0.3.3 |

Date: | 2019-08-31 |

License: | GPL (>= 2.0) |

LazyLoad: | no |

Virginie Rondeau, Juan R. Gonzalez, Yassin Mazroui, Audrey Mauguen, Amadou Diakite, Alexandre Laurent, Myriam Lopez, Agnieszka Krol and Casimir L. Sofeu

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.

Y. Mazroui, S. Mathoulin-Pelissier,P. Soubeyranb and Virginie Rondeau (2012)
General joint frailty model for recurrent event data with a dependent
terminalevent: Application to follicular lymphoma data. *Statistics in
Medecine*, **31**, 11-12, 1162-1176.

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.

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## Not run:
###--- 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")
###--- General Joint model (recurrent and terminal events) with 2 covariates ---###
data(readmission)
modJoint.general <- frailtyPenal(Surv(time,event) ~ cluster(id) + dukes +
charlson + sex + chemo + terminal(death),
formula.terminalEvent = ~ dukes + charlson + sex + chemo,
data = readmission, jointGeneral = TRUE, n.knots = 8,
kappa = c(2.11e+08, 9.53e+11))
###--- 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")
###--- Joint Nested Frailty model ---###
#-- here is generated cluster (30 clusters)
readmissionNested <- transform(readmission,group=id%%30+1)
modJointNested_Splines <- frailtyPenal(formula = Surv(t.start, t.stop, event)
~ subcluster(id) + cluster(group) + dukes + terminal(death),
formula.terminalEvent = ~dukes, data = readmissionNested, recurrentAG = TRUE,
n.knots = 8, kappa = c(9.55e+9, 1.41e+12), initialize = TRUE)
modJointNested_Weib <- frailtyPenal(Surv(t.start,t.stop,event)~subcluster(id)
+cluster(group)+dukes+ terminal(death),formula.terminalEvent=~dukes,
hazard = ('Weibull'), data=readmissionNested,recurrentAG=TRUE, initialize = FALSE)
JoiNes-GapSpline <- frailtyPenal(formula = Surv(time, event)
~ subcluster(id) + cluster(group) + dukes + terminal(death),
formula.terminalEvent = ~dukes, data = readmissionNested, recurrentAG = FALSE,
n.knots = 8, kappa = c(9.55e+9, 1.41e+12), initialize = TRUE,
init.Alpha = 1.091, Ksi = "None")
###--- 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)
###--- Joint model for longitudinal ---###
###--- data and a terminal event ---###
data(colorectal)
data(colorectalLongi)
# Survival data preparation - only terminal events
colorectalSurv <- subset(colorectal, new.lesions == 0)
model.weib.RE <- longiPenal(Surv(time1, state) ~ age + treatment + who.PS
+ prev.resection, tumor.size ~ year * treatment + age + who.PS ,
colorectalSurv, data.Longi = colorectalLongi,
random = c("1", "year"), id = "id", link = "Random-effects",
left.censoring = -3.33, hazard = "Weibull")
###--- Trivariate joint model for longitudinal ---###
###--- data, recurrent and terminal events ---###
data(colorectal)
data(colorectalLongi)
# (computation takes around 40 minutes)
model.spli.RE.cal <-trivPenal(Surv(time0, time1, new.lesions) ~ cluster(id)
+ age + treatment + who.PS + terminal(state),
formula.terminalEvent =~ age + treatment + who.PS + prev.resection,
tumor.size ~ year * treatment + age + who.PS, data = colorectal,
data.Longi = colorectalLongi, random = c("1", "year"), id = "id",
link = "Random-effects", left.censoring = -3.33, recurrentAG = TRUE,
n.knots = 6, kappa=c(0.01, 2), method.GH="Pseudo-adaptive",
n.nodes=7, init.B = c(-0.07, -0.13, -0.16, -0.17, 0.42, #recurrent events covariates
-0.23, -0.1, -0.09, -0.12, 0.8, -0.23, #terminal event covariates
3.02, -0.30, 0.05, -0.63, -0.02, -0.29, 0.11, 0.74)) #biomarker covariates
##---Surrogacy evaluation based on ganerated data with a combination
##of Monte Carlo and classical Gaussian Hermite integration.
## (Computation takes around 5 minutes)
# Generation of data to use
data.sim <- jointSurrSimul(n.obs=600, n.trial = 30,cens.adm=549.24,
alpha = 1.5, theta = 3.5, gamma = 2.5, zeta = 1, sigma.s = 0.7,
sigma.t = 0.7, rsqrt = 0.8, betas = -1.25, betat = -1.25,
full.data = 0, random.generator = 1, seed = 0, nb.reject.data = 0)
# Joint surrogate model estimation
joint.surro.sim.MCGH <- jointSurroPenal(data = data.sim, int.method = 2,
nb.mc = 300, nb.gh = 20)
## End(Not run)
``` |

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