In survival analysis, events sometimes only start to occur after a
certain delay since entry time and this delay period might vary for
different treatments or groups. While parametric delay models, like the
three-parameter Weibull distribution, might adequately describe this
process the estimation of delay via standard maximum likelihood is
severely biased in small samples. The R-package incubate
employs an
alternative estimation method called maximum product of spacings
estimation (MPSE) to estimate and test delay and other parameters in a
one or two group setting. Concretely, building on MPSE, incubate
can
The incubate
-package provides the delayed exponential distribution as
special case of the delayed Weibull distribution. We draw random samples
corresponding to two groups with different model parameters.
library("incubate")
# simulate data from exponential distribution with delay
x <- rexp_delayed(n = 13, delay1 = 1.0, rate1 = 0.8)
y <- rexp_delayed(n = 11, delay1 = 1.5, rate1 = 1.2)
We use the model function delay_model
to fit a exponential model with
delay to both groups and show the model fit.
fm <- delay_model(x, y)
plot(fm)
Inference on the model parameters is possible through confint
for
bootstrap confidence intervals and delay_test
for parameter
comparisons in a two group setting.
# confidence interval for delay-parameters
confint(fm, parm = c('delay1.x', 'delay1.y'))
#> 2.5% 97.5%
#> delay1.x 0.8060324 1.094331
#> delay1.y 1.3505534 1.753090
# test on difference in delay
# for real applications use R>=1000 bootstrap draws
delay_test <- test_diff(x, y, R = 100)
plot(delay_test)
To switch on parallel computation, e.g. for bootstrap parameter tests or
power simulations, simply set up a suitable computation plan via the
Future-API. For instance, do the following to activate four R-sessions
in the background of your local computer for computer-intensive tasks in
incubate
:
library("future")
plan(multisession, workers = 4)
That’s it. You do not have to change any function calls. incubate
is
future
-aware. Consult the future
-package on
CRAN for more information
about futures and about supported computation plans.
When you are done with the heavy computing, it is best practice to
release the parallel connections via plan(sequential)
.
The incubate
package is found on
CRAN and development
happens at Gitlab.
Use install.packages
to install incubate
from CRAN as usual, i.e.,
install.packages("incubate")
should do.
To install its latest version from the main branch on Gitlab use the following R-code:
remotes::install_gitlab("imb-dev/incubate")
To install a specific version, add the version tag after the name,
separated by a @
, e.g. to install incubate
in version v1.1.9
use
remotes::install_gitlab("imb-dev/incubate@v1.1.9")
The suffix @develop
points to the latest development version on
Gitlab.
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