Functional survival forests for multivariate longitudinal outcomes: Dynamic prediction of Alzheimer's disease progression.
Abstract
The random survival forest (RSF) is a non-parametric alternative to the Cox proportional
hazards model in modeling time-to-event data. In this article, we developed a modeling
framework to incorporate multivariate longitudinal data in the model building process
to enhance the predictive performance of RSF. To extract the essential features of
the multivariate longitudinal outcomes, two methods were adopted and compared: multivariate
functional principal component analysis and multivariate fast covariance estimation
for sparse functional data. These resulting features, which capture the trajectories
of the multiple longitudinal outcomes, are then included as time-independent predictors
in the subsequent RSF model. This non-parametric modeling framework, denoted as functional
survival forests, is better at capturing the various trends in both the longitudinal
outcomes and the survival model which may be difficult to model using only parametric
approaches. These advantages are demonstrated through simulations and applications
to the Alzheimer's Disease Neuroimaging Initiative.
Type
Journal articleSubject
Brier scoreFunctional data analysis
area under the curve
joint model
personalized prediction
survival ensembles
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https://hdl.handle.net/10161/21389Published Version (Please cite this version)
10.1177/0962280220941532Publication Info
Lin, Jeffrey; Li, Kan; & Luo, Sheng (2020). Functional survival forests for multivariate longitudinal outcomes: Dynamic prediction
of Alzheimer's disease progression. Statistical methods in medical research. pp. 962280220941532. 10.1177/0962280220941532. Retrieved from https://hdl.handle.net/10161/21389.This is constructed from limited available data and may be imprecise. To cite this
article, please review & use the official citation provided by the journal.
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Show full item recordScholars@Duke
Sheng Luo
Professor of Biostatistics & Bioinformatics

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