The multivariate Bernoulli detector: change point estimation in discrete survival analysis.

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 suffers 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 nonconjugate Bayesian nonparametric methods. We illustrate our model in simulations and on ICU data, comparing its performance with existing approaches.

Joint modelling of mental health markers through pregnancy: a Bayesian semi-parametric approach.

Maternal depression and anxiety through pregnancy have lasting societal impacts. It is thus crucial to understand the trajectories of its progression from preconception to postnatal period, and the risk factors associated with it. Within the Bayesian framework, we propose to jointly model seven outcomes, of which two are physiological and five non-physiological indicators of maternal depression and anxiety over time. We model the former two by a Gaussian process and the latter by an autoregressive model, while imposing a multidimensional Dirichlet process prior on the subject-specific random effects to account for subject heterogeneity and induce clustering. The model allows for the inclusion of covariates through a regression term. Our findings reveal four distinct clusters of trajectories of the seven health outcomes, characterising women's mental health progression from before to after pregnancy. Importantly, our results caution against the loose use of hair corticosteroids as a biomarker, or even a causal factor, for pregnancy mental health progression. Additionally, the regression analysis reveals a range of preconception determinants and risk factors for depressive and anxiety symptoms during pregnancy.