extrafrail (version 1.13)

frailty.fit: Fitted different shared frailty models

Description

frailty.fit computes the maximum likelihood estimates based on the EM algorithm for the shared gamma, inverse gaussian, weighted Lindley, Birnbaum-Saunders, truncated normal, mixture of inverse gaussian and mixture of Birbaum-Saunders frailty models.

Usage

frailty.fit(formula, data, dist.frail="gamma", dist = "np", prec = 1e-04, 
        max.iter = 1000, part=NULL)

Value

an object of class "extrafrail" is returned. The object returned for this functions is a list containing the following components:

coefficients

A named vector of coefficients

se

A named vector of the standard errors for the estimated coefficients.

t

The vector of times.

delta

The failure indicators.

id

A variable indicating the cluster which belongs each observation.

x

The regressor matrix based on cov.formula (without intercept term).

dist

The distribution assumed for the basal model.

dist.frail

The distribution assumed for the frailty variable.

tau

The Kendall's tau coefficient.

logLik

The log-likelihood function (only when the Weibull model is specified for the basal distribution).

Lambda0

The observed times and the associated cumulative hazard function (only when the non-parametric option is specified for the basal distribution)

part

the partition time (only for piecewise exponential model).

Arguments

formula

A formula that contains on the left hand side an object of the type Surv and on the right hand side a +cluster(id) statement, possibly with the covariates definition.

data

A data.frame in which the formula argument can be evaluated

dist.frail

the distribution assumed for the frailty. Supported values: gamma (GA also is valid), IG (inverse gaussian), WL (weighted Lindley), BS (Birnbaum-Saunders), TN (truncated normal), MIG (mixture of IG), MBS (mixture of BS) and GE (generalized exponential).

dist

the distribution assumed for the basal model. Supported values: weibull, pe (piecewise exponential), exponential and np (non-parametric).

prec

The convergence tolerance for parameters.

max.iter

The maximum number of iterations.

part

partition time (only for piecewise exponential distribution).

Author

Diego Gallardo, Marcelo Bourguignon and John Santibanez.

Details

For the weibull, exponential and piecewise exponential distributions as the basal model, the M1-step is performed using the optim function. For the non-parametric case, the M1-step is based on the coxph function from the survival package.

References

Gallardo, D.I., Bourguignon, M. (2022) The shared weighted Lindley frailty model for cluster failure time data. Biometrical journal, 67, e70044.

Gallardo, D.I., Bourguignon, M., Romeo, J. (2024) Birnbaum-Saunders frailty regression models for clustered survival data. Statistics and Computing, 34, 141.

Examples

# \donttest{
require(survival)
#require(frailtyHL)
data(rats, package="frailtyHL")
#Fit for WL frailty model
fit.WL <- frailty.fit(survival::Surv(time, status)~ rx+ survival::cluster(litter), 
dist.frail="WL", data = rats)
summary(fit.WL)
#Fit for gamma frailty model
fit.GA <- frailty.fit(survival::Surv(time, status) ~ rx + survival::cluster(litter), 
dist.frail="gamma", data = rats)
summary(fit.GA)
# }