risksetROC: 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 risksetROC from a survival data set

Usage

risksetROC(Stime, entry=NULL, status, marker, predict.time, method="Cox",
                       span=NULL, order=1, window="asymmetric", prop=0.5,
                       plot=TRUE, type="l", xlab="FP", ylab="TP",
                       ...)  

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
predict.time	time point of interest
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"
prop	what proportion of the time-interval to consider when doing a local Cox fitting at predict.time, needed for method="LocalCox", default is 0.5.
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 ROC 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 ROC curve is plotted with the diagonal line. Additional plot arguments can be supplied.

Value

Returns a list of the following items:

eta	unique marker values for calculation of TP and FP
TP	True Positive values corresponding to unique marker values
FP	False Positive values corresponding to unique marker values
AUC	Area Under (ROC) Curve at time predict.time

Author(s)

Paramita Saha

References

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

See Also

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

ROC.CC30=risksetROC(Stime=survival.time, status=survival.status,
                    marker=eta, predict.time=30, method="Cox",
                    main="ROC Curve", lty=2, col="red") 

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