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

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Summary
This summary is machine-generated.

This study introduces a new framework using mixed-effects location scale (MELS) modeling to identify subjects influencing health outcome variability. The method effectively detects influential subjects, including those missed by standard regression models.

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Cook’s distanceInfluential dataIntensive longitudinal dataMixed-effects location scale modelVariance modeling

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Area of Science:

  • Longitudinal data analysis
  • Statistical modeling
  • Biostatistics

Background:

  • Longitudinal health outcomes require joint modeling of mean and variability.
  • Standard mixed-effects regression models (MRMs) can detect influential subjects for outcome location.
  • Existing methods lack approaches to detect subjects influencing outcome variability (scale).

Purpose of the Study:

  • To propose a novel framework for detecting influential subjects in both the location and scale of longitudinal health outcomes.
  • To extend influence analysis beyond standard mixed-effects regression models (MRMs).
  • To provide a comprehensive method for examining subject influence on MELS model components.

Main Methods:

  • Application of mixed-effects location scale (MELS) modeling.
  • Integration of established influence measures like Cook's distance and DFBETAS.
  • Development of a framework for detailed subject influence examination on model fit and coefficient estimates.

Main Results:

  • Simulations demonstrated the framework's high accuracy (over 99%) in identifying influential subjects in common longitudinal healthcare scenarios.
  • Re-analysis of a health behavior study identified 4 influential subjects.
  • Two of the identified influential subjects were not detectable using standard MRMs.

Conclusions:

  • The proposed MELS-based framework successfully identifies influential subjects often overlooked by traditional MRMs.
  • This approach enables comprehensive analysis of all data within a single model, even in the presence of influential subjects.
  • Researchers can utilize this framework to improve the robustness and accuracy of longitudinal health outcome analyses.