Dynamic predictions with time-dependent covariates in survival analysis using joint modeling and landmarking

Author:

Rizopoulos Dimitris1ORCID,Molenberghs Geert2,Lesaffre Emmanuel M.E.H.12

Affiliation:

1. Department of Biostatistics; Erasmus Medical Center; The Netherlands

2. Interuniversity Institute for Biostatistics and Statistical Bioinformatics, KU Leuven & Universiteit Hasselt; Belgium

Funder

Nederlandse Organisatie voor Wetenschappelijk Onderzoek

Publisher

Wiley

Subject

Statistics, Probability and Uncertainty,General Medicine,Statistics and Probability

Reference31 articles.

1. Analysis of survival by tumor response;Anderson;Journal of Clinical Oncology,1983

2. Bayesian shrinkage approach for a joint model of longitudinal and survival outcomes assuming different association structures;Andrinopoulou;Statistics in Medicine,2016

3. Combined dynamic predictions using joint models of two longitudinal outcomes and competing risk data;Andrinopoulou;Statistical Methods in Medical Research,2015

4. Re-operations for aortic allograft root failure: Experience from a 21-year single-center prospective follow-up study;Bekkers;European Journal of Cardio-Thoracic Surgery,2011

5. Quantifying and comparing dynamic predictive accuracy of joint models for longitudinal marker and time-to-event in presence of censoring and competing risks;Blanche;Biometrics,2015

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