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

Lin, Jeffrey

Li, Kan

Luo, Sheng

2020-09-01T13:31:37Z

2020-09-01T13:31:37Z

2020-07-29

2020-09-01T13:31:36Z

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.

0962-2802

1477-0334

https://hdl.handle.net/10161/21389

eng

SAGE Publications

Statistical methods in medical research

10.1177/0962280220941532

Brier score

Functional data analysis

area under the curve

joint model

personalized prediction

survival ensembles

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

Journal article

Luo, Sheng|0000-0003-4214-5809

962280220941532

School of Medicine

Duke Clinical Research Institute

Biostatistics & Bioinformatics

Duke

Institutes and Centers

Basic Science Departments

Published