Concordance Index and Cox Modeling

If you find yourself modeling survival with censored data, eventually you will encounter concordance index and Cox models.  In this example, I will use R with the survcomp package and in particular the function concordance.index.  The two most important inputs to the concordance.index function are the right-censored observations from experiment and a set of numeric lifetime predictions from your model.  Right-censored data arises from many clinical studies where patients may drop out of the study or the study may end before an event (e.g., death) occurs.  So, for each patient or observation, we have two pieces of information:  a time, and whether or not an event has occurred by that time.  Thus being "right-censored" means that we may only have lower bound on that the time of an event rather than knowing it exactly.

In the first example, I will use the concordance.index function with a simple predictor to show how concordance index is affected by noise.  Here, the predictor guesses the actual correct time plus some Gaussian noise.  Since the concordance.index function expects hazard ratios and not times, the most accurate guess has nearly perfect anticorrelation, and for high noise the concordance goes to its random limit of 0.5.

## EXAMPLE 1 ## Adapted from code by A. Margolin
testObservations <- rexp(100)

## Some standard deviations for the noise to add
testSds <- seq(.01, 10, .01)

myPredictions <- foreach(i = 1:length(testSds)) %do% {
  ## Add the noise--don't allow negative observations!
  return(abs(testObservations + rnorm(100, sd=testSds[i])))
}

## Get concordance indices for each standard deviation
testCIndices <- foreach (i = 1:length(testSds)) %do% {
  testCIndex <- concordance.index(myPredictions[[i]], 
                                  testObservations,
                                  array(TRUE,100))
  return(testCIndex$c.index)
}

plot(testSds, testCIndices)


Plot 1:  Perfect Anticorrelation

In example 2, I will use the inverse function to convert the time to a rate.  Since the ordering of the predictions is exactly reversed, my concordance index now drops from 1 to 0.5.

## EXAMPLE 2 ## Perfect Correlation




testCIndices <- foreach (i = 1:length(testSds)) %do% {

  testCIndex <- concordance.index(as.list(lapply(
                                            myPredictions[i],
                                            function(x) {1/x}) 
                                           )[[1]], 
                                  testObservations, 
                                  array(TRUE,100))
  return(testCIndex$c.index)


}













plot(testSds, testCIndices)







 Plot 2:  Perfect Correlation

Suppose I observe three events for patients A, B and C at time obsA = 1 year, obsB = 2 years and obsC = 3 years.  If, in arbitrary units, I predicted predA = 1, predB = 2, and predB = 3, then my concordance index is 1 for these predictions.  Alternatively, if I predicted predA = 0.1, predB = 3, and predC = 3.1, then my concordance index is still 1 for these predictions!  Only the order mattered, not their values. So, if obsA < obsB < obsC and predA < predB < predC, then the concordance index is 1.  Alternatively, if predC < predB < predA, then my predictions are exactly anticorrelated and the concordance index is zero.  The calculation itself is described in the open-access paper, On Ranking in Survival Analysis: Bounds on the Concordance Index.  The general idea is that you may randomly select two pairs of observation and prediction.  If the smaller observation also had the smaller prediction, we score 2 points. If the predictions were tied, we score 1 point.  If the smaller observation had the larger prediction, we score no points.  At the end, we normalize by 2 times the number of trials whose observations could be compared, and obtain our concordance index.

In example 3, instead of taking the inverse, I simply multiplied all of my observations by -1.  Comparing the result to that from example 2, you would find that they are exactly identical because only order mattered. I haven't shown the duplicate plot here, but you may run the R code yourself if you don't believe me!




## EXAMPLE 3 ## Perfect Correlation




testCIndices <- foreach (i = 1:length(testSds)) %do% {

  testCIndex <- concordance.index(as.list(lapply(

                                      myPredictions[i],
                                      function(x) {-1*x})
                                   )[[1]],
                                  testObservations, 
                                  array(TRUE,100)) 
  return(testCIndex$c.index)
}








plot(testSds, testCIndices)







This means that the concordance index calculation is quite versatile in terms of the inputs.  If I used a Cox model with hazard ratio results, I could use that in my concordance index calculation to compare to the observations since the order of the hazard ratios should be exactly opposite the order of the experimental observations.  Alternatively, if I used a model that gives results that are expected lifetimes in arbitrary units, I could also use that because the order of predictions should ideally be the same as the order of observations.  The sign of (result - 0.5) using rates versus using times will be opposite, because the order of the rates versus the order of the times is opposite.

In the final example, I generate some linear data with random noise and fit it with both a linear model and Cox model.  Since all data points were a known endpoint with no censoring, the result of concordance.index is exactly the same for both models.  If censorship was present, the Cox model may have had an improved fit to the data.

## EXAMPLE 4 ## Cox and Linear Models are 
             ## Identical for Non-Censored Data

obsX <- seq(0,9.9,.1)
for (i in 1:100) {
  testObservations[i] <- testObservations[i] + i * .1
}
survObservations <- Surv(testObservations, array(TRUE,100))

obsFrame <- cbind(data.frame(obsX), data.frame(testObservations))
survFrame <- cbind(data.frame(obsX), data.frame(survObservations))

## I've made a sparse, noisy line.  I'm so proud of myself!
plot(testObservations)

## Let's fit it with linear and cox models.
linReg <- lm(testObservations ~ ., obsFrame)
coxReg <- coxph(survObservations ~ ., survFrame)

## And map them back
linPredict <- predict(linReg, data.frame(obsX))
coxPredict <- predict(coxReg, data.frame(obsX), type="risk")

## Very negative, because concordance.index wants rates
concordance.index(x=linPredict,
                  surv.time=survObservations[,"time"],
                  surv.event=survObservations[,"status"],
                  na.rm=TRUE,
                  alpha= .05)$c.index
## Returned [1] 0.09010101 in my test


## Very positive, because the rates are in the reverse order
concordance.index(x=-1.0 * linPredict,
                  surv.time=survObservations[,"time"],
                  surv.event=survObservations[,"status"],
                  na.rm=TRUE,
                  alpha= .05)$c.index
## Returned [1] 0.909899 in my test


## It's just the same as above!
concordance.index(x=coxPredict,
                  surv.time=survObservations[,"time"],
                  surv.event=survObservations[,"status"],
                  na.rm=TRUE,
                  alpha= .05)$c.index
## Returned [1] 0.909899 in my test



I found the open-access article Hazard Ratio in Clinical Trials useful in understanding the Cox model, and it is definitely worth taking a look at if you intend on doing this type of analysis.