Simulation of grouped time-to-event data with nonparametric baseline hazard and discrete shared frailty distribution
Simulation of grouped time-to-event data with nonparametric baseline hazard and discrete shared frailty distribution
This function returns a dataset generated from a semiparametric proportional hazards model with a shared discrete frailty term, for given cumulative baseline hazard function, hazard ratios, distribution of groups among latent populations, frailty values for each latent population, and randomly-generated covariate values.
sim_npdf(J, N =NULL, beta, Lambda_0_inv, p, w_values, cens_perc)
Arguments
J: number of groups in the data
N: number of individuals in each group
beta: vector of log hazard ratios
Lambda_0_inv: inverse cumulative baseline hazard function, that is, with covariate values 0 and frailty ratio 1
p: vector of K elements. The kth element gives the proportion of groups in the kth latent population of groups.
w_values: vector of K distinct frailty values, one for each latent population.
cens_perc: percentage of censored events. Censoring times are assumed to be distributed as a Normal with variance equal to 1.
Returns
A data frame with one row for each simulated individual, and the following columns:
family: the group which the individual is in (integers 1, 2, ...)
time: the simulated event time.
status: the simulated survival status. Censoring times are generated from a Normal distribution with standard deviation equal to 1 and the mean is estimated in order to guarantee the determined percentage of censored events. The event time is observed (status=1) if it is less than the censoring time, and censored otherwise (status=0).
x: matrix of covariate values, generated from a standard normal distribution.
belong: the frailty hazard ratio corresponding to the cluster of groups in which the individual's group has been allocated.