SchoenSmooth: Incident/Dynamic (I/D) ROC curve, AUC and integrated AUC...

In risksetROC: Riskset ROC Curve Estimation from Censored Survival Data

Incident/Dynamic (I/D) ROC curve, AUC and integrated AUC (iAUC) estimation of censored survival data

Description

This function smooths the Schoenfeld residuals using Epanechnikov's optimal kernel.

Usage

SchoenSmooth(fit, Stime, status, span=0.40, order=0, entry=NULL)

Arguments

fit	the result of fitting a Cox regression model, using the coxph function
Stime	Survival times in case of right censored data and exit time for left truncated data
status	Survival status
span	bandwidth parameter that controls the size of a local neighborhood
order	0 or 1, locally mean if 0 and local linear if 1
entry	entry time when left censored data is considered, default is NULL for only right censored data

Details

This function smooths the Schoenfeld residuals to get an estimate of time-varying effect of the marker using Epanechnikov's optimal kernel using either local mean or local linear smoother.

Value

Returns a list of following items:

time	failure times
beta	estimate of time-varying parameter β(t) at each unique failure time.

Author(s)

Patrick J. Heagerty

References

Heagerty, P.J., Zheng Y. (2005) Survival Model Predictive Accuracy and ROC curves Biometrics, 61, 92 – 105

Examples

data(pbc)
## considering only randomized patients
pbc1 <- pbc[1:312,]
## create new censoring variable combine 0,1 as 0, 2 as 1
survival.status <- ifelse( pbc1$status==2, 1, 0)
survival.time <- pbc1$fudays
pbc1$status1 <- survival.status
fit <- coxph( Surv(fudays,status1) ~ log(bili) +
                                     log(protime) +
                                     edema +
                                     albumin +
                                     age,
              data=pbc1 )
eta5 <- fit$linear.predictors
x <- eta5
nobs <- length(survival.time[survival.status==1])
span <- 1.5*(nobs^(-0.2))
fitCox5 <- coxph( Surv(survival.time,survival.status) ~ x )
bfnx1.1 <- SchoenSmooth( fit=fitCox5, Stime=survival.time, status=survival.status,
                       span=span, order=1)
bfnx1.0 <- SchoenSmooth( fit=fitCox5, Stime=survival.time, status=survival.status,
                       span=span, order=0)
plot(bfnx1.1$time, bfnx1.1$beta, type="l", xlab="Time", ylab="beta(t)")
lines(bfnx1.0$time, bfnx1.0$beta, lty=3)

risksetROC documentation built on June 13, 2022, 9:05 a.m.