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 prostate cancer data, comparing its performance with existing approaches.

翻译：时间事件数据通常以离散尺度记录，并包含多个竞争风险作为事件的潜在原因。在此背景下，应用单一风险的连续生存分析方法会产生有偏估计。因此，我们提出了一种针对离散时间下竞争风险的多元伯努利检测器，该检测器在原因特异性基线风险函数上构建了多元变点模型。通过对变点数量及其位置的先验设定，我们在不同风险间的变点之间施加依赖关系，同时允许数据驱动地学习变点数量。在此基础上，利用多元伯努利先验推断哪些风险参与了竞争。后验推断的重点是原因特异性风险率以及风险间的依赖关系。这种依赖关系通常源于随时间变化的个体特异性改变，且会影响所有风险。我们通过一种定制的局部-全局马尔可夫链蒙特卡洛（MCMC）算法进行完整的后验推断，该算法利用数据增广技巧以及非共轭贝叶斯非参数方法中的MCMC更新。我们通过模拟实验和前列腺癌数据验证了模型的有效性，并将其性能与现有方法进行了比较。