Simulating survival data with predefined censoring rates under a mixture of non-informative right censoring schemes

Simulation studies have been routinely used to validate the performances of statistical methods for censored survival data under various scenarios. Our previous work proposed an integrated approach of simulating right censored survival data for proportional hazards models given a set of arbitrarily distributed baseline covariates and predefined censoring rates. However, the limitations are that all study subjects are assumed to be enrolled at the same time and there is no study ending time. We extended the previous work to accommodate the more realistic scenario under which study subjects are enrolled at a constant rate during an enrollment period and are then followed until one of the following events occurs: (a) the event of interest (e.g., death or occurrence of disease); (b) the end of study period; (c) early withdraws from random censoring events, whichever comes first. To demonstrate the application of the proposed approach in practice, we generated censored survival data and assessed the impact of several factors (the magnitude of confounding, size of treatment effect, the sine distance between coefficient vectors of confounders in the treatment and outcome models, and censoring rate) on the potential bias of propensity score matching estimators in estimating conditional and marginal hazards ratios.