# Simulation of survival times In survobj: Objects to Simulate Survival Times

knitr::opts_chunk$set( collapse = TRUE, comment = "#>" )  library(survobj) library(survival)  ## Introduction Following @bender2003 and @leemis1987, simulation of survival times is possible if there is function that invert the cumulative hazard ($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 s_obj <- s_exponential(fail = 0.4, t = 2) ggplot_survival_random(s_obj, timeto =2, subjects = 1000, nsim= 10, alpha = 0.3)  ## Generation of Proportional Hazard times 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. s_obj <- s_exponential(fail = 0.4, t = 2) ggplot_survival_hr(s_obj, hr = 2, nsim = 10, subjects = 1000, timeto = 5)  ## Generation of Acceleration Failure Times 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

## Try the survobj package in your browser

Any scripts or data that you put into this service are public.

survobj documentation built on May 29, 2024, 8:34 a.m.