Survival analysis aims to estimate a time-to-event distribution from data with censored observations. Many existing methods either impose structural assumptions on the hazard function or discretize the time axis, which may limit flexibility and introduce approximation errors. We propose the Survival Diffusion Probabilistic Model (SDPM), a generative approach to continuous-time survival analysis. SDPM models the conditional distribution of the survival outcome, represented by the pair of observed time and censoring indicator, $\mathbb{P}(T,δ\mid \mathbf{x})$, using a denoising diffusion model. Under the assumption of conditionally independent censoring, conditional samples generated by the model can be transformed into survival function estimates using the Kaplan-Meier estimator. This formulation avoids parametric assumptions on the event-time distribution and does not require a discretization of the output time space. The model operates in a transformed target space, using standardized log-times and a continuous Gaussian-mixture representation of the censoring indicator. We evaluate SDPM on ten real survival datasets and compare it with five strong baselines, including tree-based, boosting-based, and neural survival models. Results show that SDPM achieves competitive predictive performance across C-index, integrated time-dependent AUC, and integrated Brier score. A study on synthetic Cox-Weibull data demonstrates that SDPM can recover the shape of an underlying continuous survival distribution more accurately than a strong nonparametric baseline when sufficiently many samples are generated. An ablation study confirms the importance of the proposed target-space transformations, which improve event-rate calibration, reduce invalid generated times, and provide consistent gains in predictive discrimination. Codes implementing the proposed model are publicly available.

翻译：生存分析旨在从含删失观测的数据中估计事件发生时间分布。现有方法或对风险函数施加结构假设，或对时间轴进行离散化，这限制了模型灵活性并引入近似误差。我们提出生存扩散概率模型（SDPM），一种面向连续时间生存分析的生成式方法。SDPM 通过去噪扩散模型建模生存结果的联合条件分布，即观测时间与删失指示变量构成的组合 $\mathbb{P}(T,δ\mid \mathbf{x})$。在条件独立删失假设下，模型生成的条件样本可通过Kaplan-Meier估计器转化为生存函数估计。该公式避免了事件时间分布的参数化假设，且无需对输出时间空间进行离散化。模型在变换后的目标空间中运行，采用标准化对数时间与删失指示变量的连续高斯混合表示。我们在十个真实生存数据集上评估SDPM，并与包括基于树、基于提升及神经网络生存模型在内的五个强基线方法进行对比。结果表明，SDPM在C-index、时间依赖AUC积分及Brier评分积分上均取得竞争性预测性能。针对合成Cox-Weibull数据的研究显示，当生成足够多样本时，SDPM比强非参数基线更准确地恢复底层连续生存分布的形状。消融研究证实了所提出目标空间变换的重要性：该变换可改进事件率校准、减少无效生成时间，并在预测判别力上带来一致性提升。实现所提模型的代码已开源。