curephEM (version 0.3.2)

survpred.cureph: Prediction for a Logistic-CoxPH Cure-Rate Model

Description

Compute Predictions and Survival Curves from a Logistic-CoxPH Cure-Rate Model

Usage

# S3 method for cureph
survpred(object, newdata , time, center = F)
basehaz.cureph(object)
# S3 method for survpred.cureph
plot(x, pooled = T, censor = x$censored,...)

Value

logistic.linear.predict

the linear predictions for cure indicator logistic model.

logistic.linear.prob

the estimated probability for NOT being cured.

cox.linear.predict

the linear predictions for event time CoxPH model.

cox.cumhaz

the baseline cummulative hazard for event time CoxPH model.

surv.cox

the marginal survival table for the cure-rate model.

surv.cureph

the conditional survival table for event time CoxPH model.

...

The object will also contain the following: censored, origin and end.

Arguments

object

a cureph object.

newdata

a data frame with the same variable names as those that appear in the cureph formula. It is also valid to use a vector, if the data frame would consist of a single row.

The curve(s) produced will be representative of a cohort whose covariates correspond to the values in newdata. Default is the data frame used in the original fit for prediction and the mean of the covariates for survival curve. See cureph.object.

time

a numeric vector; all the time points to be evaluated in the survival table in addition to the observed event times.

center

logical; if TRUE, the baseline survival is evaluated at the mean of the covariates. Otherwise, it is evaluated at zero.

x

a survpred.cureph object.

pooled

logical; if TRUE, the marginal survival curve(s) for the cure-rate model will be plotted. Otherwise, the conditional survival curve(s) in the Cox part will be plotted.

censor

a numeric vector of the observed censoring times. If not NULL, a cross will mark each censored point on the survival curve.

...

Other parameters to match the generic S3 method.

Author

Jue (Marquis) Hou

Details

The means of factor variables are not computed. Instead, the baseline level is used.

The NPMLE baseline survival is estimated in a Breslow-Nelson-Aalen fashion.

See Also

cureph

Examples

library(curephEM)

# A simulated data set
data("sim.cureph.data")

# Fit logistic-CoxPH cure-rate model
fit=cureph(Surv.cure(time,time2,event,origin=0,end=20)~Z1+Z2+Z3+Z4,data=sim.cureph.data)

# The survival-prediction object
mysurv = survpred(fit,center=TRUE)

oldpar = par()
par(mfrow=c(1,2))
plot(mysurv)
plot(mysurv,FALSE)
par(oldpar)