\name{survivROC}
\alias{survivROC}
\title{
Generates the ROC curve at a given time point given the observed and predicted survival data in the presence of censored subjects.
}
\description{
The function generates the Receiver-Operator Curve (ROC) at the specified time point using predicted and observed data in the presence of censored subjects. It so plots 
(1 - specificity) against the (specificity) at the designated cut off points. It is based on Patrick Heagerty's survivalROC function in the survivalROC package.
}
\usage{
survivROC(Stime, status, marker, entry = NULL, predict.time, cut.values = NULL, plot = TRUE)
}
\arguments{
  \item{Stime}{
The observed survival times of the patients.
}
  \item{status}{
The censoring status of the patient. 1 for a censored patient, and 0 for a patient who has an event.
}
  \item{marker}{
The predicted survival time of the patients.
}
  \item{entry}{
The time of entry of the patients, set to NULL by default.
}
  \item{predict.time}{
The time point for which the ROC curve is to be plotted.
}
  \item{cut.values}{
The cut off values for which the ROC curves are to be constructed.
}
  \item{plot}{
A logical argument that specifies whether a plot is to be generated (TRUE) or not (FALSE). The argument is set to TRUE by default.
}
}
\details{
This function is basically the survivROC function in Patrick Heagerty's survivROC package, with slight modifications to it to better suit our purpose. Unlike Heagerty's 
function it only performs the calculations using the KM estimator and does not provide any other methods as options.
}
\value{
\item{cut.values}{the cut off values for which the sensitivity and (1-specificity) were calculated}
\item{Comp.Specificity}{(1 - Specificity) as was calculated for every cut off value.}
\item{Sensitivity}{The Sensitivity as was calculated for every cut off value}
\item{predict.time}{The time point for which the ROC curve is calculated}
\item{Survival}{The value of the estimated survivor function (using the KM estimator) at "predict.time"}
\item{AUC}{The value of the area under the estimated ROC curve}
}
\references{
Heagerty,P., Lumely T. & Pepe M.(2000). Time-dependent ROC curves for censored survival data & a diagnostic marker. Biometrics, 56(2), 337-344.
}
\author{
Douaa Mugahid
}
\note{
It is important to note that using the Kaplan-Meier estimator as the method for estimating the survival function results in non-monotonous curves in some instances.
}

\seealso{
\code{\link{survivAURC}}
}
\examples{
True_STs <- c(1.416667,2.75,2.416667,2.583333,2.166667,2.5,2.5,1.833333,1.25,0.6666667,1,6.583333,6.5,6.666667,2.75,1.666667,1.166667,2.833333,3.583333,6.166667,6.166667,
3.416667,6.083333,1.833333,5.583333,0.75,5.75,5.5,0.5833333,7.666667,5,2.833333,1.333333,5.083333,0.8333333,1.5,4.75,3.416667,4.666667,1.916667,4.666667,7.416667,0.9166667,
1.083333,3.75,3.25,3,2.416667,2.75,2.5,2.666667,4.5,4.416667,1.5,0.8333333,3.166667,3.833333,3.833333,0.4166667,3.333333,2.75,3.083333,0.3333333,0.25,0.6666667,1.833333,
2.333333,3.416667,3.416667,3,0.6666667,0.75,2.166667,1,1.416667,1.333333,1.166667,1.166667,0.4166667,1.25,1.166667,1.083333)
Predicted_STs <- c(6.030591,6.014457,3.545584,5.414229,6.41576,9.393992,5.542331,6.890859,8.090213,4.98545,2.77357,6.275699,9.163978,7.511511,9.531218,7.63715,10.08977,
11.12364,3.982502,5.441881,12.61404,12.21851,17.05850,12.78141,16.22795,21.48544,6.281354,13.83925,8.859929,6.104142,8.255909,2.335526,6.564962,2.335761,9.33772,12.62540,
10.97276,15.63089,8.01967,5.817267,5.59897,4.340784,32.40319,33.74123,27.45024,26.31024,26.88833,24.34707,32.06541,38.90473,17.37102,15.11059,8.772035,14.24816,7.852889,
7.79996,5.601459,2.802408,35.77047,24.34717,30.65796,25.93927,20.64544,22.04807,19.15037,23.83430,1.876557,3.937208,6.526354,5.886377,9.301074,12.4657,14.49783,15.41502,
2.860931,2.541947,4.543111,4.525553,4.148272,3.986912,6.246755,6.89523)
censored <- c(0,1,1,1,1,0,1,1,0,1,0,1,1,1,1,0,0,0,0,1,1,1,1,0,1,0,1,1,0,1,1,0,0,1,0,0,1,0,1,0,1,1,0,0,1,1,1,0,1,1,1,1,1,0,1,1,1,1,0,1,1,1,1,0,0,0,1,1,1,1,0,0,1,0,1,1,1,1,1,1,1,
1)
survivROC(Stime=True_STs,status=censored, marker=Predicted_STs,predict.time=5)
}
\keyword{ ROC curves }
\keyword{ survival analysis }