## ----inference-examples-------------------------------------------------------
library(cifmodeling)
example_data <- data.frame(
  time = 1:12,
  status = c(1, 0, 2, 1, 2, 0, 1, 2, 0, 1, 0, 0),
  weight = c(1, 1.5, 0.8, 1.2, 1, 0.7, 1.3, 1, 0.9, 1.1, 1, 1)
)

# AJ with IF-t and equal-allocation degrees of freedom.
fit_edf <- cifcurve(
  Event(time, status) ~ 1, data = example_data, weights = "weight",
  outcome.type = "competing-risk", error = "if",
  conf.dist = "t", df.method = "equal", conf.type = "log-log"
)
at_five <- summary(fit_edf, times = 5)
data.frame(
  time = at_five$time, CIF = 1 - at_five$surv,
  SE = at_five$std.err, lower = 1 - at_five$upper,
  upper = 1 - at_five$lower, df = at_five$df,
  status = at_five$conf.status
)

# KM for the composite of either event, with FG-t.
fit_fg <- cifcurve(
  Event(time, status > 0) ~ 1, data = example_data,
  outcome.type = "survival", error = "fay",
  conf.dist = "t", conf.type = "arcsin"
)

## ----inference-plot, eval=FALSE-----------------------------------------------
# cifplot(fit_edf, outcome.type = "competing-risk", add.risktable = FALSE)