The concordance statistic compute the agreement between an observed response and a predictor. It is closely related to Kendall's tau-a and tau-b, Goodman's gamma, and Somers' d, all of which can also be calculated from the results of this function.
concordance(object, ...)
# S3 method for formula
concordance(object, data, weights, subset, na.action,
cluster, ymin, ymax, timewt= c("n", "S", "S/G", "n/G", "n/G2", "I"),
influence=0, ranks = FALSE, reverse=FALSE, timefix=TRUE, keepstrata=10, ...)
# S3 method for lm
concordance(object, ..., newdata, cluster, ymin, ymax,
influence=0, ranks=FALSE, timefix=TRUE, keepstrata=10)
# S3 method for coxph
concordance(object, ..., newdata, cluster, ymin, ymax,
timewt= c("n", "S", "S/G", "n/G", "n/G2", "I"), influence=0,
ranks=FALSE, timefix=FALSE, keepstrata=10)
# S3 method for survreg
concordance(object, ..., newdata, cluster, ymin, ymax,
timewt= c("n", "S", "S/G", "n/G", "n/G2", "I"), influence=0,
ranks=FALSE, timefix=FALSE, keepstrata=10)
An object of class concordance containing the following
components:
the estimated concordance value or values
a vector containing the number of concordant pairs, discordant, tied on x but not y, tied on y but not x, and tied on both x and y
the number of observations
a vector containing the estimated variance of the concordance based on the infinitesimal jackknife (IJ) method. If there are multiple models it contains the estimtated variance/covariance matrix.
a vector containing the estimated variance(s) of the
concordance values, based on the variance formula for the associated
score test from a proportional hazards model. (This was the primary
variance used in the survConcordance function.)
optional, the vector of leverage estimates for the concordance
optional, the matrix of leverage values for each of the counts, one row per observation
optional, a data frame containing the Somers' d rank at each event time, along with the time weight, case weight of the observation with an event, and variance (contribution to the proportional hazards model information matrix). A weighted mean of the ranks equals Somer's d.
a fitted model or a formula. The formula should be of
the form y ~x or y ~ x + strata(z) with a single
numeric or survival response and a single predictor.
Counts of concordant, discordant and tied pairs
are computed separately per stratum, and then added.
a data.frame in which to interpret the variables named in
the formula, or in the subset and the weights
argument. Only applicable if object is a formula.
optional vector of case weights.
Only applicable if object is a formula.
expression indicating which subset of the rows of data should be used in
the fit. Only applicable if object is a formula.
a missing-data filter function. This is applied to the model.frame
after any subset argument has been used. Default is
options()\$na.action. Only applicable if object is a formula.
multiple fitted models are allowed. Only applicable if
object is a model object.
optional, a new data frame in which to evaluate (but not refit) the models
optional grouping vector for calculating the robust variance
compute the concordance over the restricted range ymin <= y <= ymax. (For survival data this is a time range.)
the weighting to be applied. The overall statistic is a weighted mean over event times.
1= return the dfbeta vector, 2= return the full influence matrix, 3 = return both
if TRUE, return a data frame containing the individual ranks that make up the overall score.
if TRUE then assume that larger x values predict
smaller response values y; a proportional hazards model is
the common example of this.
if the response is a Surv object, correct for possible rounding error; otherwise this argument has no effect. See the vignette on tied times for more explanation. For the coxph and survreg methods this issue will have already been addressed in the parent routine, so should not be revisited.
either TRUE, FALSE, or an integer value.
Computations are always done within stratum, then added. If the
total number of strata greater than keepstrata, or
keepstrata=FALSE, those subtotals are not kept in the output.
Terry Therneau
At each event time, compute the rank of the subject who had the
event as compared to all others with a longer survival, where the
rank is value between 0 and 1. The concordance is a weighted mean
of these values, determined by the timewt option.
For uncensored data each unique response value is compared to all
those which are larger.
Using the default value for timewt gives the area
under the receiver operating curve (AUC) for a binary response,
and (d+1)/2 when y is continuous, where d is Somers' d.
For a survival time, timewt of n gives Harrell's c-statistic,
which is closely related to the Gehan-Wilcoxon test,
S corresponds to the Peto-Wilcoxon, n/G2 is the weighted advocated
by Umo, and S/G the weighting proposed by Schemper.
When the number of strata is very large, such as in a conditional
logistic regression for instance (clogit function), a much
faster computation is available when the individual strata results
are not retained. In the more general case the keepstrata = 10
default simply keeps the printout managable.
coxph
fit1 <- coxph(Surv(ptime, pstat) ~ age + sex + mspike, mgus2)
concordance(fit1, timewt="n")
# logistic regression
fit2 <- glm(pstat ~ age + sex + mspike, binomial, data= mgus2)
concordance(fit2) # equal to the AUC
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