risksetAUC {risksetROC}

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);