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Kaplan-Meier Curves as a Data Frame

Source: R/as.data.frame.kmcurve_fast.R
as.data.frame.kmcurve_fast.Rd

Returns the Kaplan-Meier curves of a kmcurve_fast object as a data frame, one row per group and distinct observed time, for drawing the curves with other graphics software or for tabulation.

Usage

# S3 method for class 'kmcurve_fast'
as.data.frame(
  x,
  row.names = NULL,
  optional = FALSE,
  conf.level = 0.95,
  conf.type = "log",
  ...
)

Arguments

x

A kmcurve_fast object from kmcurve_fast.

row.names

NULL or a character vector of row names.

optional

Ignored; the column names are fixed.

conf.level

Confidence level of the pointwise limits. Defaults to 0.95.

conf.type

Type of the pointwise limits: "plain", "log" (default), or "log-log", as in survfit_fast.

...

Further arguments, currently ignored.

Value

A data frame with the columns group (the group label, as printed by print.kmcurve_fast), time, n.risk, n.event, n.censor, surv, std.err, lower, and upper. In the two-group case the rows of the control group come first.

Details

For each group the rows are the distinct observed times (event and censoring times) in ascending order, preceded by a row at time 0 with survival 1, so that a step function drawn through the rows starts at 1 and ends at the largest observed time. At each time, n.risk is the number of subjects with an observed time at or after it, n.event and n.censor are the numbers of events and censored times at it, and surv is the Kaplan-Meier estimate just after it. std.err is the Greenwood standard error on the survival scale, the std.err reported by summary() of a survival::survfit() object, and lower and upper are the pointwise confidence limits of the type conf.type, as drawn by plot.kmcurve_fast. At a time at which every subject at risk fails, the estimate is 0 and std.err and the limits are 0, as in survfit_fast, where summary() reports a NaN standard error and NA limits. Apart from the row at time 0, the times, counts, and survival estimates are those reported by summary() of a survival::survfit() object with censored = TRUE.

See also

kmcurve_fast, plot.kmcurve_fast

Examples

set.seed(1)
n <- 50
t0 <- rexp(n, log(2) / 12)
t1 <- rexp(n, log(2) / 18)
cens <- runif(2 * n, 0, 30)
time  <- pmin(c(t0, t1), cens)
event <- as.integer(c(t0, t1) <= cens)
group <- rep(c(0, 1), each = n)

km <- as.data.frame(kmcurve_fast(time, event, group, control = 0))
head(km)
#>   group      time n.risk n.event n.censor      surv    std.err     lower
#> 1     0 0.0000000     50       0        0 1.0000000 0.00000000 1.0000000
#> 2     0 0.6452054     50       1        0 0.9800000 0.01979899 0.9419529
#> 3     0 1.0290310     49       1        0 0.9600000 0.02771281 0.9071919
#> 4     0 1.3938266     48       0        1 0.9600000 0.02771281 0.9071919
#> 5     0 1.8363654     47       1        0 0.9395745 0.03382296 0.8755673
#> 6     0 2.4201832     46       1        0 0.9191489 0.03876760 0.8462217
#>      upper
#> 1 1.000000
#> 2 1.000000
#> 3 1.000000
#> 4 1.000000
#> 5 1.000000
#> 6 0.998361