survMisc (version 0.4.6)

quantile: Quantiles and median for Surv, survfit and coxph objects

Description

Extends stats::quantile and stats::quantile to work with Surv, survfit and coxph objects.

Usage

quantile(x, ...)
## S3 method for class 'Surv':
quantile(x, ..., q = c(25, 50, 75), CI = TRUE,
  alpha = 0.05, ci = c("log", "lin", "asr"))
## S3 method for class 'survfit':
quantile(x, ..., q = c(25, 50, 75), CI = TRUE,
  alpha = 0.05, ci = c("log", "lin", "asr"))
## S3 method for class 'coxph':
quantile(x, ..., q = c(25, 50, 75), CI = TRUE,
  alpha = 0.05, ci = c("log", "lin", "asr"))
median(x, ...)
## S3 method for class 'Surv':
median(x, ..., CI = FALSE, alpha = 0.05, ci = c("log",
  "lin", "asr"))
## S3 method for class 'survfit':
median(x, ..., CI = FALSE, alpha = 0.05, ci = c("log",
  "lin", "asr"))
## S3 method for class 'coxph':
median(x, ..., CI = FALSE, alpha = 0.05, ci = c("log",
  "lin", "asr"))

Arguments

x

A Surv, survfit or coxph object.

...

Additional arguments (not implemented).

q

(for quantile) Vector of quantiles (expressed as percentage). For the median, q=50.

CI

Include confidence interval. Defaults are CI=TRUE for quantile and CI=FALSE for median.

alpha

Significance level $\alpha$.

ci

Confidence interval. One of: log (the default), linear or arcsine-square root.

Value

• For quantile: A data.table (or a list of data.tables, one per stratum), with columns:
• q
  quantile
• t
  time
• If CI = TRUE then upper and lower confidence intervals, as per argument ci).
• l
  lower confidence limit
• u
  upper confidence limit
• For median: A data.table with columns:
• t
  time
• s
  stratum
• If CI = TRUE then a list of data.tables, one per stratum, as above.

References

Examples for quantiles are from: Klein J, Moeschberger M 2003 Survival Analysis, 2nd edition. New York: Springer. Example 4.2, pg 121.

See Also

Confidence intervals are calculated as shown in the pointwise confidence intervals in ci.

Examples

data(bmt, package="KMsurv")
b1 <- bmt[bmt$group==1, ] # ALL patients
s1 <- Surv(time=b1$t2, event=b1$d3)
quantile(s1)
b1 <- bmt[bmt$group==2, ] # AML low-risk patients
s1 <- Surv(time=b1$t2, event=b1$d3)
quantile(s1)
b1 <- bmt[bmt$group==3, ] # AML high-risk patients
s1 <- Surv(time=b1$t2, event=b1$d3)
quantile(s1)
###
s1 <- survfit(Surv(t2, d3) ~ group, data=bmt)
quantile(s1)
c1 <- coxph(Surv(t2, d3)~ group, data=bmt)
quantile(c1)
b1 <- bmt[bmt$group==1, ] # ALL patients
s1 <- Surv(time=b1$t2, event=b1$d3)
median(s1)
median(s1, CI=TRUE)
data(bmt, package="KMsurv")
b1 <- bmt[bmt$group==1, ] # ALL patients
s1 <- survfit(Surv(t2, d3)~ group, data=bmt)
median(s1)
median(s1, ci="asr", CI=TRUE)
c1 <- coxph(Surv(t2, d3) ~ group, data=bmt)
median(c1)