risksetAUC | R Documentation |
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);
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