Improved dynamic predictions from joint models of longitudinal and survival data with time†varying effects using P†splines
Eleni†Rosalina Andrinopoulou,
Paul H. C. Eilers,
Johanna J. M. Takkenberg and
Dimitris Rizopoulos
Biometrics, 2018, vol. 74, issue 2, 685-693
Abstract:
In the field of cardio†thoracic surgery, valve function is monitored over time after surgery. The motivation for our research comes from a study which includes patients who received a human tissue valve in the aortic position. These patients are followed prospectively over time by standardized echocardiographic assessment of valve function. Loss of follow†up could be caused by valve intervention or the death of the patient. One of the main characteristics of the human valve is that its durability is limited. Therefore, it is of interest to obtain a prognostic model in order for the physicians to scan trends in valve function over time and plan their next intervention, accounting for the characteristics of the data. Several authors have focused on deriving predictions under the standard joint modeling of longitudinal and survival data framework that assumes a constant effect for the coefficient that links the longitudinal and survival outcomes. However, in our case, this may be a restrictive assumption. Since the valve degenerates, the association between the biomarker with survival may change over time. To improve dynamic predictions, we propose a Bayesian joint model that allows a time†varying coefficient to link the longitudinal and the survival processes, using P†splines. We evaluate the performance of the model in terms of discrimination and calibration, while accounting for censoring.
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)
Downloads: (external link)
https://doi.org/10.1111/biom.12814
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:bla:biomet:v:74:y:2018:i:2:p:685-693
Ordering information: This journal article can be ordered from
http://www.blackwell ... bs.asp?ref=0006-341X
Access Statistics for this article
More articles in Biometrics from The International Biometric Society
Bibliographic data for series maintained by Wiley Content Delivery ().