adjustedCurves (version 0.11.2)

cif_aalen_johansen: Group-Specific Aalen-Johansen CIFs

Description

This page explains the details of estimating standard Aalen-Johansen cumulative incidence functions, stratified by the group variable (method="aalen_johansen" in the adjustedcif function). All regular arguments of the adjustedcif function can be used. Further arguments specific to this method are listed below.

NO adjustment for any confounders are made. This function is included only for reference and should not be used when confounder adjusted CIFs are desired.

Value

Adds the following additional objects to the output of the adjustedsurv function:

• cuminc_object: The object returned by the cuminc function.

Arguments

...

Further arguments passed to cuminc.

Author

The wrapper function was written by Robin Denz, the cuminc function (which this wrapper is build around) was written by other people. See ?cuminc for more details.

Details

• Type of Adjustment: NO adjustments are made. This is just a stratified Aalen-Johansen estimator.

• Doubly-Robust: Estimates are not Doubly-Robust.

• Categorical groups: Any number of levels in variable are allowed. Must be a factor variable.

• Approximate Variance: Calculations to approximate the variance and confidence intervals are available.

• Allowed Time Values: Allows both continuous and integer time.

• Bounded Estimates: Estimates are guaranteed to be bounded in the 0 to 1 probability range.

• Monotone Function: Estimates are guaranteed to be monotone.

• Dependencies: This method relies on the the cmprsk package.

This function is just a convenient wrapper around the cuminc function. See ?cuminc or the cited literature for more details.

References

Odd O. Aalen and Søren Johansen (1978). "An Empirical Transition Matrix for Non-Homogeneous Markov Chains Based on Censored Observations". In: Scandinavian Journal of Statistics 5.3, pp. 141-150

See Also

cuminc

Examples

library(adjustedCurves)

if (requireNamespace("cmprsk")) {

library(cmprsk)

set.seed(42)

# simulate some data as example
sim_dat <- sim_confounded_crisk(n=50, max_t=5)
sim_dat$group <- as.factor(sim_dat$group)

# calculate un-adjusted aalen-johansen estimates
adjcif <- adjustedcif(data=sim_dat,
                      variable="group",
                      ev_time="time",
                      event="event",
                      cause=1,
                      method="aalen_johansen")

# plot the curves
plot(adjcif)
}