cif2.ks {surv2sample} | R Documentation |
Compares cumulative incidence functions (CIF) for one failure cause in two samples of censored competing risks data using the Kolmogorov–Smirnov-type test.
cif2.ks(x, group, cause = 1, nsim = 2000, nsim.plot = 50)
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 . |
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).
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.
David Kraus (http://www.davidkraus.net/)
Lin, D. Y. (1997) Non-parametric inference for cumulative incidence functions in competing risks studies. Stat. Med. 16, 901–910.
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.
## 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)