Weighted-average (non-parametric) IPCW cumulative incidence estimate

Estimates the marginal cumulative incidence of event type 1 by computing stratum-specific Aalen-Johansen estimates and then combining them with sample-proportion weights. This is the non-parametric IPCW approach.

Usage

cuminc_waverage_cr(
  dat,
  esttimes = seq(from = 0, to = 10, length.out = 100),
  time_var = "t",
  event_var = "delta",
  covariate = "z1"
)

Arguments

dat

A wide-format competing risks data frame containing the columns specified by time_var, event_var, and covariate.

esttimes

Numeric vector of times at which to return estimates. Defaults to 100 equally spaced points from 0 to 10.

time_var

Character string. Name of the event/censoring time column. Default is "t".

event_var

Character string. Name of the event indicator column. Default is "delta".

covariate

Character string. Name of the stratification covariate column. Default is "z1".

Value

A numeric vector of weighted-average cumulative incidence estimates at esttimes. Values beyond the minimum of the stratum-specific maximum follow-up times are set to NA.

Examples

set.seed(42)
dat <- sim_data_cr(n = 200, censoring = "baseline")
cuminc_waverage_cr(dat, esttimes = seq(0, 5, 0.5))
#>  [1] 0.0000000 0.2549215 0.3706694 0.4349190 0.4462671 0.4689078 0.4689078
#>  [8] 0.4689078        NA        NA        NA