risksetAUC: 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 creates risksetAUC from a survival data set

Usage

risksetAUC(Stime, entry=NULL, status, marker, method="Cox",
                       span=NULL, order=1, window="asymmetric",
                       tmax, weight="rescale", plot=TRUE, type="l",
                       xlab="Time", ylab="AUC", ...)  

Arguments

Stime	For right censored data, this is the follow up time. For left truncated data, this is the ending time for the interval.
entry	For left truncated data, this is the entry time of the interval. The default is set to NULL for right censored data.
status	survival status, 1 if had an event and 0 otherwise
marker	marker
method	either of "Cox", "LocalCox" and "Schoenfeld", default is "Cox"
span	bandwidth parameter that controls the size of a local neighborhood, needed for method="LocalCox" or method="Schoenfeld"
order	0 or 1, locally mean if 0 and local linear if 1, needed for method="Schoenfeld", default is 1
window	either of "asymmetric" or "symmetric", default is asymmetric, needed for method="LocalCox"
tmax	maximum time to be considered for calculation of AUC
weight	either of "rescale" or "conditional". If weight="rescale", then weights are rescaled so that the sum is unity. If weight="conditional" both the event times are assumed to be less than tmax
plot	TRUE or FALSE, default is TRUE
type	default is "l", can be either of "p" for points, "l" for line, "b" for both
xlab	label for x-axis
ylab	label for y-axis
...	additional plot arguments

Details

This function creates and plots AUC based on incident/dynamic definition of Heagerty, et. al. based on a survival data and marker values. If proportional hazard is assumed then method="Cox" can be used. In case of non-proportional hazard, either of "LocalCox" or "Schoenfeld" can be used. These two methods differ in how the smoothing is done. If plot="TRUE" then the AUC curve is plotted against time (till tmax+1). Additional plot arguments can be supplied.

Value

Returns a list of the following items:

utimes	ordered unique failure times
St	estimated survival probability at utimes
AUC	Area under ROC curve at utimes
Cindex	Cindex

Author(s)

Paramita Saha

References

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

See Also

IntegrateAUC(), weightedKM(), llCoxReg(), SchoenSmooth(), CoxWeights()

Examples

library(MASS)
data(VA)
survival.time=VA$stime
survival.status=VA$status
score <- VA$Karn
cell.type <- factor(VA$cell)
tx <- as.integer( VA$treat==1 )
age <- VA$age
survival.status[survival.time>500 ] <- 0
survival.time[survival.time>500 ] <- 500
fit0 <- coxph( Surv(survival.time,survival.status)
        ~ score + cell.type + tx + age, na.action=na.omit )
eta <- fit0$linear.predictor
tmax=365
AUC.CC=risksetAUC(Stime=survival.time,
       status=survival.status, marker=eta, method="Cox", tmax=tmax); 

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