Improved dynamic predictions from joint models of longitudinal and survival data with time-varying effects using P-splines-Reference-Cited by-同舟云学术

Improved dynamic predictions from joint models of longitudinal and survival data with time-varying effects using P-splines

Published:2017-11-01 Issue:2 Volume:74 Page:685-693
ISSN:0006-341X
Container-title:Biometrics
language:en
Short-container-title:Biom

Author:

Andrinopoulou Eleni-Rosalina¹^ORCID,Eilers Paul H. C.¹,Takkenberg Johanna J. M.²,Rizopoulos Dimitris¹

Affiliation:

1. Department of Biostatistics; Erasmus MC, Rotterdam The Netherlands

2. Department of Cardiothoracic Surgery; Erasmus MC, Rotterdam The Netherlands

Publisher

Wiley

Subject

Applied Mathematics,General Agricultural and Biological Sciences,General Immunology and Microbiology,General Biochemistry, Genetics and Molecular Biology,General Medicine,Statistics and Probability

Link

http://onlinelibrary.wiley.com/wol1/doi/10.1111/biom.12814/fullpdf

Reference27 articles.

1. Combined dynamic predictions using joint models of two longitudinal outcomes and competing risk data;Andrinopoulou;Statistical Methods in Medical Research,2017

2. Andrinopoulou , E.-R. Rizopoulos , D. 2016 Bayesian shrinkage approach for a joint model of longitudinal and survival outcomes assuming different association structures https://doi.org/10.1002/sim.7027

3. Dynamic prediction of outcome for patients with severe aortic stenosis: Application of joint models for longitudinal and time-to-event data;Andrinopoulou;BMC Cardiovascular Disorders,2015

4. An introduction to mixed models and joint modeling: Analysis of valve function over time;Andrinopoulou;The Annals of Thoracic Surgery,2012

5. Joint modeling of two longitudinal outcomes and competing risk data;Andrinopoulou;Statistics in Medicine,2014

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