cif2.ks {surv2sample}R Documentation

Kolmogorov–Smirnov Two-Sample Test for Cumulative Incidence Functions

Description

Compares cumulative incidence functions (CIF) for one failure cause in two samples of censored competing risks data using the Kolmogorov–Smirnov-type test.

Usage

cif2.ks(x, group, cause = 1, nsim = 2000, nsim.plot = 50)

Arguments

x a "Survcomp" object, as returned by the Survcomp function.
group a vector indicating to which group each observation belongs. May contain values 1 and 2 only.
cause For which cause of failure should the CIFs be compared?
nsim the number of simulations to approximate the p-value. Must be positive.
nsim.plot the number of simulated paths of the test process to be returned (for possible plotting). Must be at most nsim.

Details

The test compares cumulative incidence functions F_1(t,k), F_2(t,k) for a particular failure cause k.

The test statistic is the maximum absolute difference of the two cumulative incidence functions. Its asymptotic distribution is complicated, therefore the martingale-based simulation approximation is employed. See Lin (1997).

Value

A list with class attributes "cif2.int" and "lwy.test", with components:

stat the test statistic.
pval.sim the simulation based p-value.
test.process the test process (difference of the two CIFs).
test.process.sim simulated paths of the test process (a matrix with nsim.plot columns).
time sorted times.

Further components are cause, nsim, nsim.plot, the same as on input.

Author(s)

David Kraus (http://www.davidkraus.net/)

References

Lin, D. Y. (1997) Non-parametric inference for cumulative incidence functions in competing risks studies. Stat. Med. 16, 901–910.

See Also

See the plot method inherited from the class "lwy.test".

See cif and plot.cif for estimation and plotting of CIFs, cif2.int, cif2.logrank and cif2.neyman for other two-sample tests.

Examples

## bone marrow transplant data
data(bmt1)

## compare CIFs for cause 1 (relapse)
## print results
print(a <- cif2.ks(Survcomp(bmt1$time, bmt1$event), bmt1$donor,
      cause = 1))
## plot the test process and simulated paths
plot(a)

## compare CIFs for cause 2 (death in remission)
## print results
print(a <- cif2.ks(Survcomp(bmt1$time, bmt1$event), bmt1$donor,
    cause = 2))
## plot the test process and simulated paths
plot(a)

[Package surv2sample version 0.1-2 Index]