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

Description Usage Arguments Details Value Author(s) References Examples

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

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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 (marker,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 marker

FP

False Positive corresponding to the cut offs in marker

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

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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)
  

Example output



survivalROC documentation built on May 1, 2019, 7:49 p.m.