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

We propose a joint model for a time-to-event outcome and a quantile of acontinuous response repeatedly measured over time. The quantile and survivalprocesses are associated via shared latent and manifest variables. Our jointmodel provides a flexible approach to handle informative drop-out in quantileregression. A general Monte Carlo Expectation Maximization strategy based onimportance sampling is proposed, which is directly applicable under anydistributional assumption for the longitudinal outcome and random effects, andparametric and non-parametric assumptions for the baseline hazard. Modelproperties are illustrated through a simulation study and an application to anoriginal data set about dilated cardiomyopathies.

Longitudinal quantile regression in presence of informative drop-out through longitudinal-survival joint modeling