quantile.survfit {survival} | R Documentation |

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

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

`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 |

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.

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.

Terry Therneau

`survfit`

, `print.survfit`

,
`qsurvreg`

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

[Package *survival* version 2.44-1.1 Index]