English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Time-to-event data are often recorded on a discrete scale with multiple,competing risks as potential causes for the event. In this context, applicationof continuous survival analysis methods with a single risk suffer from biasedestimation. Therefore, we propose the Multivariate Bernoulli detector forcompeting risks with discrete times involving a multivariate change point modelon the cause-specific baseline hazards. Through the prior on the number ofchange points and their location, we impose dependence between change pointsacross risks, as well as allowing for data-driven learning of their number.Then, conditionally on these change points, a Multivariate Bernoulli prior isused to infer which risks are involved. Focus of posterior inference iscause-specific hazard rates and dependence across risks. Such dependence isoften present due to subject-specific changes across time that affect allrisks. Full posterior inference is performed through a tailored local-globalMarkov chain Monte Carlo (MCMC) algorithm, which exploits a data augmentationtrick and MCMC updates from non-conjugate Bayesian nonparametric methods. Weillustrate our model in simulations and on prostate cancer data, comparing itsperformance with existing approaches.

The Multivariate Bernoulli detector: Change point estimation in discrete survival analysis