Create survival curvesSource:
Create survival curves
# S3 method for survTerms survfit(survTerms, ...)
survTermsobject: survival terms obtained after running
Arguments passed on to
expression indicating which subset of the rows of data should be used in the fit. This can be a logical vector (which is replicated to have length equal to the number of observations), a numeric vector indicating which observation numbers are to be included (or excluded if negative), or a character vector of row names to be included. All observations are included by default.
a missing-data filter function. This is applied to the
model.frameafter any subset argument has been used. Default is
a scalar parameter that controls the type of test.
process times through the
aeqSurvfunction to eliminate potential roundoff issues.
survfit object. See
survfit.object for details. Methods
survfit objects are
A survival curve is based on a tabulation of the number at risk and
number of events at each unique death time. When time is a floating
point number the definition of "unique" is subject to interpretation.
The code uses factor() to define the set.
For further details see the documentation for the appropriate method, i.e.,
A survfit object may contain a single curve, a set of curves, or a
Predicted curves from a
coxph model have one row for each
stratum in the Cox model fit and one column for each specified
Curves from a multi-state model have one row for each stratum and
a column for each state, the strata correspond to predictors on the
right hand side of the equation. The default printing and plotting
order for curves is by column, as with other matrices.
Curves can be subscripted using either a single or double subscript. If the set of curves is a matrix, as in the above, and one of the dimensions is 1 then the code allows a single subscript to be used. (That is, it is not quite as general as using a single subscript for a numeric matrix.)
library("survival") clinical <- read.table(text = "2549 NA ii female 840 NA i female NA 1204 iv male NA 383 iv female 1293 NA iii male NA 1355 ii male") names(clinical) <- c("patient.days_to_last_followup", "patient.days_to_death", "patient.stage_event.pathologic_stage", "patient.gender") timeStart <- "days_to_death" event <- "days_to_death" formulaStr <- "patient.stage_event.pathologic_stage + patient.gender" survTerms <- processSurvTerms(clinical, censoring="right", event, timeStart, formulaStr=formulaStr) survfit(survTerms) #> Call: survfit(formula = survTerms$form, data = survTerms$survTime) #> #> n events median #> patient.stage_event.pathologic_stage=i, patient.gender=female 1 0 NA #> patient.stage_event.pathologic_stage=ii, patient.gender=female 1 0 NA #> patient.stage_event.pathologic_stage=ii, patient.gender=male 1 1 1355 #> patient.stage_event.pathologic_stage=iii, patient.gender=male 1 0 NA #> patient.stage_event.pathologic_stage=iv, patient.gender=female 1 1 383 #> patient.stage_event.pathologic_stage=iv, patient.gender=male 1 1 1204 #> 0.95LCL 0.95UCL #> patient.stage_event.pathologic_stage=i, patient.gender=female NA NA #> patient.stage_event.pathologic_stage=ii, patient.gender=female NA NA #> patient.stage_event.pathologic_stage=ii, patient.gender=male NA NA #> patient.stage_event.pathologic_stage=iii, patient.gender=male NA NA #> patient.stage_event.pathologic_stage=iv, patient.gender=female NA NA #> patient.stage_event.pathologic_stage=iv, patient.gender=male NA NA