  Synthetic data generation holds considerable promise, offering avenues to
enhance privacy, fairness, and data accessibility. Despite the availability of
various methods for generating synthetic tabular data, challenges persist,
particularly in specialized applications such as survival analysis. One
significant obstacle in survival data generation is censoring, which manifests
as not knowing the precise timing of observed (target) events for certain
instances. Existing methods face difficulties in accurately reproducing the
real distribution of event times for both observed (uncensored) events and
censored events, i.e., the generated event-time distributions do not accurately
match the underlying distributions of the real data. So motivated, we propose a
simple paradigm to produce synthetic survival data by generating covariates
conditioned on event times (and censoring indicators), thus allowing one to
reuse existing conditional generative models for tabular data without
significant computational overhead, and without making assumptions about the
(usually unknown) generation mechanism underlying censoring. We evaluate this
method via extensive experiments on real-world datasets. Our methodology
outperforms multiple competitive baselines at generating survival data, while
improving the performance of downstream survival models trained on it and
tested on real data.
