The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
A Bayesian semiparametric multivariate joint model for multiple longitudinal outcomes and a time‐to‐event
Abstract
Motivated by a real data example on renal graft failure, we propose a new semiparametric multivariate joint model that relates multiple longitudinal outcomes to a time‐to‐event. To allow for greater flexibility, key components of the model are modelled nonparametrically. In particular, for the subject‐specific longitudinal evolutions we use a spline‐based approach, the baseline risk function is assumed piecewise constant, and the distribution of the latent terms is modelled using a Dirichlet Process prior formulation. Additionally, we discuss the choice of a suitable parameterization, from a practitioner's point of view, to relate the longitudinal process to the survival outcome. Specifically, we present three main families of parameterizations, discuss their features, and present tools to choose between them. Copyright © 2011 John Wiley & Sons, Ltd.
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Citations
Cites
Modeling Survival Data: Extending the Cox Model
Therneau, T. M., & Grambsch, P. M. (2000) · Statistics for Biology and Health · doi:10.1007/978-1-4757-3294-8 · Cited by 8,674
Cited by
Dynamic Predictions and Prospective Accuracy in Joint Models for Longitudinal and Time-to-Event Data
Rizopoulos, D. (2011) · Biometrics · doi:10.1111/j.1541-0420.2010.01546.x · Cited by 458
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Metadata
- Authors
- Rizopoulos, D., & Ghosh, P.
- Venue
- Statistics in Medicine
- Publisher
- Wiley
- Published
- 2011-02-21
- Volume
- 30
- Issue
- 12
- Pages
- 1366-1380
- ISSN
- 0277-6715 (ISSN-L)
- 1097-0258
- OpenAlex
- W2030840862
- Cited by
- 243
- References
- 1
- FWCI
- 9.74
- Type
- journal article
- Language
- en
- Open access
- closed
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