Detecting influential subjects in intensive longitudinal data using mixed-effects location scale models.

ABSTRACT:

Background

Collection of intensive longitudinal health outcomes allows joint modeling of their mean (location) and variability (scale). Focusing on the location of the outcome, measures to detect influential subjects in longitudinal data using standard mixed-effects regression models (MRMs) have been widely discussed. However, no existing approach enables the detection of subjects that heavily influence the scale of the outcome.

Methods

We propose applying mixed-effects location scale (MELS) modeling combined with commonly used influence measures such as Cook's distance and DFBETAS to fill this gap. In this paper, we provide a framework for researchers to follow when trying to detect influential subjects for both the scale and location of the outcome. The framework allows de

SUBMITTER: Zhang X 

PROVIDER: S-EPMC10585916 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature