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Nonlinear association structures in flexible Bayesian additive joint models.
Stat. Med. 37, 4771-4788 (2018)
Joint models of longitudinal and survival data have become an important tool for modeling associations between longitudinal biomarkers and event processes. The association between marker and log hazard is assumed to be linear in existing shared random effects models, with this assumption usually remaining unchecked. We present an extended framework of flexible additive joint models that allows the estimation of nonlinear covariate specific associations by making use of Bayesian P-splines. Our joint models are estimated in a Bayesian framework using structured additive predictors for all model components, allowing for great flexibility in the specification of smooth nonlinear, time-varying, and random effects terms for longitudinal submodel, survival submodel, and their association. The ability to capture truly linear and nonlinear associations is assessed in simulations and illustrated on the widely studied biomedical data on the rare fatal liver disease primary biliary cirrhosis. All methods are implemented in the R package bamlss to facilitate the application of this flexible joint model in practice.
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Publication type
Article: Journal article
Document type
Scientific Article
Keywords
Joint Model ; Longitudinal Data ; Nonlinear Association ; P-splines ; Time-to-event Data; To-event Data; Survival-data; R Package; Regression; Splines; Predictions
ISSN (print) / ISBN
0277-6715
e-ISSN
1097-0258
Journal
Statistics in Medicine
Quellenangaben
Volume: 37,
Issue: 30,
Pages: 4771-4788
Publisher
Wiley
Publishing Place
111 River St, Hoboken 07030-5774, Nj Usa
Non-patent literature
Publications
Reviewing status
Peer reviewed
Institute(s)
Institute of Diabetes Research Type 1 (IDF)