Diffusion models can effectively handle continuous-time survival analysis by modeling censored outcomes directly, avoiding parametric assumptions and discretization errors that limit traditional survival methods.
SDPM uses diffusion models to estimate time-to-event distributions from data with censored observations, without requiring assumptions about the hazard function or discretizing time. The model generates samples that can be converted to survival curves, achieving competitive performance on real datasets while accurately recovering underlying continuous distributions.