Time-to-event data are often recorded on a discrete scale with multiple, competing risks as potential causes for the event. In this context, application of continuous survival analysis methods with a single risk suffer from biased estimation. Therefore, we propose the Multivariate Bernoulli detector for competing risks with discrete times involving a multivariate change point model on the cause-specific baseline hazards. Through the prior on the number of change points and their location, we impose dependence between change points across risks, as well as allowing for data-driven learning of their number. Then, conditionally on these change points, a Multivariate Bernoulli prior is used to infer which risks are involved. Focus of posterior inference is cause-specific hazard rates and dependence across risks. Such dependence is often present due to subject-specific changes across time that affect all risks. Full posterior inference is performed through a tailored local-global Markov chain Monte Carlo (MCMC) algorithm, which exploits a data augmentation trick and MCMC updates from non-conjugate Bayesian nonparametric methods. We illustrate our model in simulations and on ICU data, comparing its performance with existing approaches.

翻译：时间-事件数据通常以离散尺度记录，存在多个竞争风险作为事件的潜在原因。在此背景下，应用仅考虑单一风险的连续生存分析方法会导致估计偏差。因此，我们针对离散时间竞争风险提出多元伯努利检测器，该模型在风险特异性基线风险函数上建立多元变点模型。通过对变点数量及其位置的先验设置，我们实现了跨风险变点间的依赖性，并允许数据驱动学习变点数量。在给定这些变点的条件下，采用多元伯努利先验推断涉及的具体风险。后验推断的核心是风险特异性危险率及跨风险依赖性——这种依赖性常因个体随时间变化影响所有风险而存在。我们通过定制的局部-全局马尔可夫链蒙特卡罗（MCMC）算法进行全后验推断，该算法结合了数据增强技巧与非共轭贝叶斯非参数方法的MCMC更新策略。通过模拟实验和ICU数据验证模型性能，并与现有方法进行比较。