Random survival forests with multivariate longitudinal endogenous covariates
Author:
Affiliation:
1. Univ. Bordeaux, INSERM, BPH, U1219, Bordeaux, France
2. The George Institute for Global Health, UNSW Sydney, Australia
3. School of Population Health, UNSW Sydney, Australia
4. Univ. Bordeaux, INSERM, INRIA, BPH, U1219, Bordeaux, France
Abstract
Funder
Agence Nationale de la Recherche
French government in the framework of the PIA3
University of Bordeaux’s IdEx ”Investments for the Future”
Publisher
SAGE Publications
Subject
Health Information Management,Statistics and Probability,Epidemiology
Link
http://journals.sagepub.com/doi/pdf/10.1177/09622802231206477
Reference29 articles.
1. Development and validation of a dynamic prognostic tool for prostate cancer recurrence using repeated measures of posttreatment PSA: a joint modeling approach
2. Dynamic predictions of long-term kidney graft failure: an information tool promoting patient-centred care
3. Landmark Models for Optimizing the Use of Repeated Measurements of Risk Factors in Electronic Health Records to Predict Future Disease Risk
4. Joint Models for Longitudinal and Time-to-Event Data
5. Dynamic Prediction by Landmarking in Event History Analysis
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