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

In the field of cardio-thoracic surgery, valve function is monitored overtime after surgery. The motivation for our research comes from a study whichincludes patients who received a human tissue valve in the aortic position.These patients are followed prospectively over time by standardizedechocardiographic assessment of valve function. Loss of follow-up could becaused by valve intervention or the death of the patient. One of the maincharacteristics of the human valve is that its durability is limited.Therefore, it is of interest to obtain a prognostic model in order for thephysicians to scan trends in valve function over time and plan their nextintervention, accounting for the characteristics of the data.  Several authors have focused on deriving predictions under the standard jointmodeling of longitudinal and survival data framework that assumes a constanteffect for the coefficient that links the longitudinal and survival outcomes.However, in our case this may be a restrictive assumption. Since the valvedegenerates, the association between the biomarker with survival may changeover time.  To improve dynamic predictions we propose a Bayesian joint model that allowsa time-varying coefficient to link the longitudinal and the survival processes,using P-splines. We evaluate the performance of the model in terms ofdiscrimination and calibration, while accounting for censoring.

Improved Dynamic Predictions from Joint Models of Longitudinal and Survival Data with Time-Varying Effects using P-splines