D) ROC curve, AUC and integrated AUC...
In risksetROC: Riskset ROC curve estimation from censored survival data

Description Usage Arguments Details Value Author(s) References See Also Examples

This function creates risksetAUC from a survival data set

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", ...)

`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

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.

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

Paramita Saha

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

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

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);