beta: vector of regression coefficient for coxph model
fvar: frailty variance value(currently the function works for gamma frailty only)
bhdist: distribution of survival time at baseline e.g. "weibull","exponential","llogistic"
X: model matrix for the coxPH model with particular choice of beta
fdist: distribution of frailty terms e.g. "gamma"
...: user can assume the shape and scale parameter of baseline survival distribution
Returns
simulated survival data for a single transition
Details
The process for simulation of multistate survival data is described in our manuscript. As the process includes transition through different states and it involves simulating survival time in different transition. So we have demonstrated the code for simulation of simple survival model. Suppose we want to simulate a survival data with parametric baseline hazard and parametric frailty model. The hazard model is as follows:
hi(t)=zih0(t)exp(xiβ);i=1,2,3,...,n
where the baseline survival time follow Weibull distribution and the hazard is
h0(t)=ρλtρ−1
. Similarly we can have Gompertz, log logistic distribution. The following are the formula for hazard and cummulative hazard function For exponential: h0(t)=λ and H0(t)=λt;λ>0
Gompertz: h0(t)=λexp(γt) and H0(t)=γλ(exp(γt)−1);λ,γ>0
Vishwakarma, G. K., Bhattacherjee, A., Rajbongshi, B. K., & Tripathy, A. (2024). Censored imputation of time to event outcome through survival proximity score method. Journal of Computational and Applied Mathematics, 116103;
Bhattacharjee, A., Vishwakarma, G. K., Tripathy, A., & Rajbongshi, B. K. (2024). Competing risk multistate censored data modeling by propensity score matching method. Scientific Reports, 14(1), 4368.
See Also
cphGM
Author(s)
Atanu Bhattacharjee, Bhrigu Kumar Rajbongshi and Gajendra K. Vishwakarma