survivalROC (version 1.0.1)

survivalROC.C: Time-dependent ROC curve estimation from censored survival data

Description

This function creates time-dependent ROC curve from censored survival data using the Nearest Neighbor Estimation (NNE) method of Heagerty, Lumley and Pepe, 2000

Usage

survivalROC.C(Stime, status, marker, predict.time, span)

Arguments

Stime

Event time or censoring time for subjects

status

Indicator of status, 1 if death or event, 0 otherwise

marker

Predictor or marker value

predict.time

Time point of the ROC curve

span

Span for the NNE

Value

• Returns a list of the following items:
• cut.values
  unique marker values for calculation of TP and FP
• TP
  TP corresponding to the cut off in x
• FP
  FP corresponding to the cut off in x
• predict.time
  time point of interest
• Survival
  Kaplan-Meier survival estimate at predict.time
• AUC
  Area Under (ROC) Curve at time predict.time

Details

Suppose we have censored survival data along with a baseline marker value and we want to see how well the marker predicts the survival time for the subjects in the dataset. In particular, suppose we have survival times in days and we want to see how well the marker predicts the one-year survival (PredictTime=365 days). This function returns the unique marker values, sensitivity (True positive or TP), (1-specificity) (False positive or FP) and Kaplan-Meier survival estimate corresponding to the time point of interest (PredictTime). The (FP,TP) values then can be used to construct ROC curve at the time point of interest.

References

Heagerty, P.J., Lumley, T., Pepe, M. S. (2000) Time-dependent ROC Curves for Censored Survival Data and a Diagnostic Marker Biometrics, 56, 337 -- 344

Examples

data(mayo)

nobs <- NROW(mayo)
cutoff <- 365
Staltscore4 <- NULL
Mayo.fit4 <- survivalROC.C( Stime = mayo$time,  
      status = mayo$censor,      
      marker = mayo$mayoscore4,     
      predict.time = cutoff,      
      span = 0.25*nobs^(-0.20))
Staltscore4 <- Mayo.fit4$Survival
plot(Mayo.fit4$FP, Mayo.fit4$TP, type = "l",
xlim = c(0,1), ylim = c(0,1),
xlab = paste( "FP 
 AUC =",round(Mayo.fit4$AUC,3)),
ylab = "TP",main = "Year = 1" )
abline(0,1)