mixcure.penal.profile.CI.nested: mixcure.penal.profile.CI.nested

In ChangchangXu-LTRI/Mixcure: Maximum (penalized) likelihood estimation of Weibull mixture cure model

mixcure.penal.profile.CI.nested

Description

Profile likelihood confidence interval (PLCI) at alpha level (alpha=0.05 as default) for estimated parameter (MLE or FT-PLE). The PLCI endpoints of single parameter theta, are identified as intersects of profile likelihood function of theta and maximum loglikelihood value subtract 1/2 times chi square distribution (df=1) at level alpha. For FT-PLE the reduced model, nested deviance method was applied to determine the dimension of information matrix dim(I) in the penalty.

Usage

mixcure.penal.profile.CI.nested(formula, data, init, pl, apct = 0.05, LRT.pval = F, iterlim=200)

Arguments

formula	MC model specification in the form of a conventional survival model, i.e., Surv()~. Note the whatever x variables specified in the latency part of the model are assumed to be also regressed on in the incidence part, and vice versa.
data	a data object in the form of dataframe.
init	a vector of initial values input for the optimization of the vector of parameters specified in the likelihood. Usually, specified in the order of 1) regression parameters for the incidence part, 2) regression parameters for the latency part, and 3) the shape parameter of Weibull hazard function for the latency part.
pl	if TRUE, uses FT-PL; otherwise uses the usual likelihood for the model.
iterlim	specifies the number of maximum iterations for model parameter optimization. DEFAULT set to 200.
apct	specifies the alpha level of confidence interval bounds. DEFAULT set to 0.05.
LRT.pval	specifies if needs to calculate LRT p-values at the same time. DEFAULT set to FALSE.

Details

see mixcure.penal.est

Value

The mixcure.penal.profile.CI.nested function returns an object of class 'mixcure.plci' that encompasses a list of the followings:

coefficients

contains 3 tables of parameter estimates with lower bounds and upper bounds of PLCIs: i) 'CURE' table for regression parameters of the incidence part; ii) 'SURVIVAL' table for regression parameters of the latency part; iii) 'ALPHA' table for shape parameter of the Weibull baseline hazard function in the latency part.

Author(s)

Changchang Xu

References

Bull (2007), Heinze (2013)

Examples

# Begin Example

data(ANNbcBMdat1)
# Fit the MC model using maximum likelihoodlizards.
mc.mle.plci1 <- mixcure.penal.profile.CI.nested(Surv(Time, CENS == 1) ~ Her2,data=ANNbcBMdat1,init=c(1,-0.1,-10,1,1), pl=F)
# Now the bias-reduced fit:
mc.ple.plci1 <- mixcure.penal.profile.CI.nested(Surv(Time, CENS == 1) ~ Her2,data=ANNbcBMdat1,init=c(1,-0.1,-10,1,1), pl=T)

ChangchangXu-LTRI/Mixcure documentation built on April 22, 2022, 3:33 p.m.