The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.
Following Bender, Augustin, and Blettner
(2003) and Leemis (1987),
simulation of survival times is possible if there is function that
invert the cumulative hazard (\(H^{-1}\)), Random survival times for a
baseline distribution can be generated from an uniform distribution
between 0-1 \(U\) as: \[ T = H^{-1}(-log(U)) \] For a survival
distribution object, this can be accomplished with the function
rsurv(s_object, n)
which will generate n
number of random draws from the distribution s_object
. All
objects of the s_distribution family implements a function that inverts
the survival time with the function invCum_Hfx()
The function ggplot_survival_random()
helps to graph
Kaplan-Meier graphs and cumulative hazard of simulated times from the
distribution
Survival times with hazard proportional to the baseline hazard can be simulated \[ T = H^{-1}\left(\frac{-log(U)}{HR}\right) \] where \(HR\) is a hazard ratio.
The function rsurv_hr(s_object, hr)
can generate random
number with hazards proportionals to the baseline hazard. The function
produce as many numbers as the length of the hr vector. for example:
s_obj <- s_exponential(fail = 0.4, t = 2)
group <- c(rep(0,500), rep(1,500))
hr_vector <- c(rep(1,500),rep(2,500))
times <- rsurvhr(s_obj, hr_vector)
plot(survfit(Surv(times)~group), xlim=c(0,5))
The function ggplot_survival_hr()
can plot simulated data
under proportional hazard assumption.
Survival times with accelerated failure time to the baseline hazard can be simulated \[ T = \frac{H^{-1}(-log(U))}{AFT}\] where \(AFT\) is a acceleration factor, meaning for example an AFT of 2 have events two times quicker than the baseline
The function rsurv_aft(s_object, aft)
can generate
random numbers accelerated by an AFT factor. The function produce as
many numbers as the length of the aft vector. for example:
s_obj <- s_lognormal(scale = 2, shape = 0.5)
ggplot_survival_aft(s_obj, aft = 2, nsim = 10, subjects = 1000, timeto = 5)
In this example, the scale parameter of the Log-Normal distribution represents the mean time and it this simulation and accelerated factor of 2 move the average median from 2 to 1
If the proportional hazard and the accelerated failure is combined
and accelerated hazard time is generated. This can be accomplished with
the function rsurvah()
function and the
ggplot_random_ah()
functions
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.