Dynamic prediction in functional concurrent regression with an application to child growth.

ABSTRACT: In many studies, it is of interest to predict the future trajectory of subjects based on their historical data, referred to as dynamic prediction. Mixed effects models have traditionally been used for dynamic prediction. However, the commonly used random intercept and slope model is often not sufficiently flexible for modeling subject-specific trajectories. In addition, there may be useful exposures/predictors of interest that are measured concurrently with the outcome, complicating dynamic prediction. To address these problems, we propose a dynamic functional concurrent regression model to handle the case where both the functional response and the functional predictors are irregularly measured. Currently, such a model cannot be fit by existing software. We apply the model to dynamically p

SUBMITTER: Leroux A 

PROVIDER: S-EPMC5847461 | biostudies-literature | 2018 Apr

REPOSITORIES: biostudies-literature