survivalROC {survivalROC}R Documentation

Time-dependent ROC curve estimation from censored survival data

Description

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

Usage

survivalROC(Stime, status, marker, entry = NULL, predict.time, cut.values =
NULL, method = "NNE", lambda = NULL, span = NULL, window =
"symmetric")

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
entry Entry time for the subjects
predict.time Time point of the ROC curve
cut.values marker values to use as a cut-off for calculation of sensitivity and specificity
method Method for fitting joint distribution of (x,t), either of KM or NNE, the default method is NNE
lambda smoothing parameter for NNE
span Span for the NNE, need either lambda or span for NNE
window window for NNE, either of symmetric or asymmetric

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 (predict.time=365 days). This function roc.KM.calc(), returns the unique marker values, TP (True Positive), FP (False Positive), Kaplan-Meier survival estimate corresponding to the time point of interest (predict.time) and AUC (Area Under (ROC) Curve) at the time point of interest.

Value

Returns a list of the following items:

cut.values unique marker values for calculation of TP and FP
TP True Positive corresponding to the cut offs in x
FP False Positive corresponding to the cut offs 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

Author(s)

Patrick J. Heagerty

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
  ## MAYOSCORE 4, METHOD = NNE
  Mayo4.1= survivalROC(Stime=mayo$time,  
    status=mayo$censor,      
    marker = mayo$mayoscore4,     
    predict.time = cutoff,span = 0.25*nobs^(-0.20) )
  plot(Mayo4.1$FP, Mayo4.1$TP, type="l", xlim=c(0,1), ylim=c(0,1),   
  xlab=paste( "FP", "\n", "AUC = ",round(Mayo4.1$AUC,3)), 
  ylab="TP",main="Mayoscore 4, Method = NNE \n  Year = 1")
  abline(0,1)

  ## MAYOSCORE 4, METHOD = KM
  Mayo4.2= survivalROC(Stime=mayo$time,  
    status=mayo$censor,      
    marker = mayo$mayoscore4,     
    predict.time =  cutoff, method="KM")
  plot(Mayo4.2$FP, Mayo4.2$TP, type="l", xlim=c(0,1), ylim=c(0,1),   
  xlab=paste( "FP", "\n", "AUC = ",round(Mayo4.2$AUC,3)), 
  ylab="TP",main="Mayoscore 4, Method = KM \n Year = 1")
  abline(0,1)
  

[Package survivalROC version 1.0.0 Index]