Compared to the default summary() function, surv_summary()
creates a data frame containing a nice summary from
survfit results.
surv_summary(x, data = NULL)
| x | an object of class survfit. |
|---|---|
| data | a dataset used to fit survival curves. If not supplied then data will be extracted from 'fit' object. |
An object of class 'surv_summary', which is a data frame with the following columns:
time: the time points at which the curve has a step.
n.risk: the number of subjects at risk at t.
n.event: the number of events that occur at time t.
n.censor: number of censored events.
surv: estimate of survival.
std.err: standard error of survival.
upper: upper end of confidence interval.
lower: lower end of confidence interval.
strata: stratification of survival curves.
In a situation, where survival curves have been fitted with one or more
variables, surv_summary object contains extra columns representing the
variables. This makes it possible to facet the output of
ggsurvplot by strata or by some combinations of factors.
surv_summary object has also an attribut named 'table' containing information about the survival curves, including medians of survival with confidence intervals, as well as, the total number of subjects and the number of event in each curve.
# Fit survival curves require("survival") fit <- survfit(Surv(time, status) ~ rx + adhere, data = colon) # Summarize res.sum <- surv_summary(fit, data = colon) head(res.sum)#> time n.risk n.event n.censor surv std.err upper lower #> 1 20 536 1 0 0.9981343 0.001867414 1.0000000 0.9944878 #> 2 43 535 1 0 0.9962687 0.002643394 1.0000000 0.9911204 #> 3 45 534 1 0 0.9944030 0.003240519 1.0000000 0.9881072 #> 4 59 533 1 0 0.9925373 0.003745345 0.9998501 0.9852780 #> 5 72 532 1 0 0.9906716 0.004191364 0.9988435 0.9825667 #> 6 77 531 1 0 0.9888060 0.004595738 0.9977529 0.9799393 #> strata rx adhere #> 1 rx=Obs, adhere=0 Obs 0 #> 2 rx=Obs, adhere=0 Obs 0 #> 3 rx=Obs, adhere=0 Obs 0 #> 4 rx=Obs, adhere=0 Obs 0 #> 5 rx=Obs, adhere=0 Obs 0 #> 6 rx=Obs, adhere=0 Obs 0#> records n.max n.start events *rmean *se(rmean) median #> rx=Obs, adhere=0 536 536 536 287 1884.796 57.33119 1896.0 #> rx=Obs, adhere=1 94 94 94 58 1611.102 138.22808 1031.0 #> rx=Lev, adhere=0 522 522 522 269 1890.034 60.48272 2012.0 #> rx=Lev, adhere=1 98 98 98 64 1642.734 125.01246 1161.5 #> rx=Lev+5FU, adhere=0 530 530 530 203 2285.457 55.43665 NA #> rx=Lev+5FU, adhere=1 78 78 78 39 1946.302 152.86022 2174.0 #> 0.95LCL 0.95UCL #> rx=Obs, adhere=0 1447 2351 #> rx=Obs, adhere=1 726 2077 #> rx=Lev, adhere=0 1298 NA #> rx=Lev, adhere=1 851 1895 #> rx=Lev+5FU, adhere=0 NA NA #> rx=Lev+5FU, adhere=1 993 NA