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BP-based model survival curves

Source: R/survfit.R
survfit.spbp.Rd

Compute survival curves for a fitted spbp model.

Usage

# S3 method for class 'spbp'
survfit(
  formula,
  newdata = NULL,
  times = NULL,
  se.fit = TRUE,
  interval = 0.95,
  type = c("log", "log-log", "plain"),
  interval.type = c("hpd", "quantile"),
  monotone = NULL,
  tidy = FALSE,
  baseline = FALSE,
  ...
)

# S3 method for class 'survfitbp'
as.data.frame(x, row.names = NULL, optional = FALSE, ...)

Arguments

formula

An object of class "spbp" returned by spbp.

newdata

Optional data frame used to obtain survival curves for specific covariate values.

times

Optional evaluation times: a Surv object (legacy), or a non-negative numeric vector (unique values are used; suitable for smooth ggplot2::geom_line plots via predict.spbp or as.data.frame.survfitbp).

se.fit

Logical; if TRUE, compute standard errors.

interval

Confidence level for intervals (e.g. 0.95).

type

Character; confidence interval transformation. One of "log", "log-log", or "plain".

interval.type

For Bayesian fits only: "hpd" (default) or "quantile" (equal-tailed).

monotone

Logical; for Bayesian fits, enforce non-increasing credible-band limits over time so ribbons plot smoothly with ggplot2::geom_line. Defaults to TRUE when approach = "bayes".

tidy

Logical; if TRUE, return a data.frame for ggplot2. instead of a "survfit" object.

baseline

Logical; if TRUE, return the baseline survival curve \(S_0(t)\) at observed event times (or times) with no covariate effect. When tidy = TRUE, returns a simple data.frame with columns time and surv.

...

Further arguments passed to survfit.coxph for the reference Cox object (e.g. conf.type is ignored; use type instead).

x

Object from survfit.spbp.

row.names, optional

Unused; included for S3 consistency.

Value

An object of class "survfit" (with classes survfitbp, survfitcox, survfit).

A data.frame with columns id (curve index), time, surv, lower, upper, cumhaz, std.err.

Functions

See also

spbp, survfit, predict.spbp, survfit.spbp (as.data.frame method).

Examples

library(spsurv)
data(veteran, package = "survival")
#> Warning: data set ‘veteran’ not found
fit <- bpph(Surv(time, status) ~ karno + factor(celltype), data = veteran)
survfit(fit)
#> Call: survfit.spbp(formula = fit)
#> 
#>        n events median 0.95LCL 0.95UCL
#> [1,] 137    128   77.5    43.1      NA

data(veteran, package = "survival")
#> Warning: data set ‘veteran’ not found
fit <- bpph(Surv(time, status) ~ karno, data = veteran, approach = "mle", init = 0)
sf <- survfit(fit, times = seq(0, max(veteran$time), length.out = 80))
#> Warning: Bernstein-polynomial (gamma) information matrix is ill-conditioned (kappa = 1.04e+12, degree = 12); delta-method survival standard errors and confidence bands may be unreliable. Try refitting with a lower Bernstein degree (argument `degree` to bpph(), bppo(), bpaft(), or spbp()).
head(as.data.frame(sf))
#>   id     time      surv        lower upper    cumhaz    std.err
#> 1  1  0.00000 1.0000000 1.0000000000     1 0.0000000 0.00000000
#> 2  1 12.64557 0.8947161 0.7998515928     1 0.1112489 0.05718484
#> 3  1 25.29114 0.7912094 0.4045850767     1 0.2341926 0.34220047
#> 4  1 37.93671 0.6947400 0.0887711859     1 0.3642176 1.04975170
#> 5  1 50.58228 0.6079767 0.0068732687     1 0.4976187 2.28703019
#> 6  1 63.22785 0.5317991 0.0001672713     1 0.6314896 4.11456738