Functional survival forests for multivariate longitudinal outcomes: Dynamic prediction of Alzheimer's disease progression.

Published online

Journal Article

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.

Full Text

Duke Authors

Cited Authors

  • Lin, J; Li, K; Luo, S

Published Date

  • July 29, 2020

Published In

Start / End Page

  • 962280220941532 -

PubMed ID

  • 32726189

Pubmed Central ID

  • 32726189

Electronic International Standard Serial Number (EISSN)

  • 1477-0334

Digital Object Identifier (DOI)

  • 10.1177/0962280220941532

Language

  • eng

Conference Location

  • England