survival (version 2.44-1.1)

quantile.survfit: Quantiles from a survfit object

Description

Retrieve quantiles and confidence intervals for them from a survfit object.

Usage

# S3 method for survfit
quantile(x, probs = c(0.25, 0.5, 0.75), conf.int = TRUE,
  tolerance= sqrt(.Machine$double.eps), ...)
# S3 method for survfitms
quantile(x, probs = c(0.25, 0.5, 0.75), conf.int = TRUE,
  tolerance= sqrt(.Machine$double.eps), ...)

Arguments

x

a result of the survfit function

probs

numeric vector of probabilities with values in [0,1]

conf.int

should lower and upper confidence limits be returned?

tolerance

tolerance for checking that the survival curve exactly equals one of the quantiles

...

optional arguments for other methods

Value

The quantiles will be a vector if the survfit object contains only a single curve, otherwise it will be a matrix or array. In this case the last dimension will index the quantiles.

If confidence limits are requested, then result will be a list with components quantile, lower, and upper, otherwise it is the vector or matrix of quantiles.

Details

The kth quantile for a survival curve S(t) is the location at which a horizontal line at height p= 1-k intersects the plot of S(t). Since S(t) is a step function, it is possible for the curve to have a horizontal segment at exactly 1-k, in which case the midpoint of the horizontal segment is returned. This mirrors the standard behavior of the median when data is uncensored. If the survival curve does not fall to 1-k, then that quantile is undefined.

In order to be consistent with other quantile functions, the argument prob of this function applies to the cumulative distribution function F(t) = 1-S(t).

Confidence limits for the values are based on the intersection of the horizontal line at 1-k with the upper and lower limits for the survival curve. Hence confidence limits use the same p-value as was in effect when the curve was created, and will differ depending on the conf.type option of survfit. If the survival curves have no confidence bands, confidence limits for the quantiles are not available.

When a horizontal segment of the survival curve exactly matches one of the requested quantiles the returned value will be the midpoint of the horizontal segment; this agrees with the usual definition of a median for uncensored data. Since the survival curve is computed as a series of products, however, there may be round off error. Assume for instance a sample of size 20 with no tied times and no censoring. The survival curve after the 10th death is (19/20)(18/19)(17/18) ... (10/11) = 10/20, but the computed result will not be exactly 0.5. Any horizontal segment whose absolute difference with a requested percentile is less than tolerance is considered to be an exact match.

See Also

survfit, print.survfit, qsurvreg

Examples

# NOT RUN {
fit <- survfit(Surv(time, status) ~ ph.ecog, data=lung)
quantile(fit)

cfit <- coxph(Surv(time, status) ~ age + strata(ph.ecog), data=lung)
csurv<- survfit(cfit, newdata=data.frame(age=c(40, 60, 80)),
                  conf.type ="none")
temp <- quantile(csurv, 1:5/10)
temp[2,3,]  # quantiles for second level of ph.ecog, age=80
quantile(csurv[2,3], 1:5/10)  # quantiles of a single curve, same result
# }