Create survival curves

Usage

# S3 method for class 'survTerms'
survfit(formula, ...)

Arguments

formula

survTerms object: survival terms obtained after running processSurvTerms (see examples)

...

Arguments passed on to survival::survdiff

subset: 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.
na.action: a missing-data filter function. This is applied to the model.frame after any subset argument has been used. Default is options()$na.action.
rho: a scalar parameter that controls the type of test.
timefix: process times through the aeqSurv function to eliminate potential roundoff issues.

Value

survfit object. See survfit.object for details. Methods defined for survfit objects are print, plot, lines, and points.

Details

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., ?survfit.formula or ?survfit.coxph.

A survfit object may contain a single curve, a set of curves (vector), a matrix of curves, or even a 3 way array: dim(fit) will reveal the dimensions. Predicted curves from a coxph model have one row for each stratum in the Cox model fit and one column for each specified covariate set. 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.

Examples

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 = formula$form, data = formula$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