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Daniel McNeish1

  • 1Department of Psychology, Arizona State University.

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Intensive longitudinal studies often have missing data. A new dynamic structural equation model approach effectively handles missing not at random (MNAR) data, improving analysis of sensitive health behaviors.

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Area of Science:

  • Psychological research methods
  • Longitudinal data analysis
  • Statistical modeling

Background:

  • Intensive longitudinal designs capture rapid changes in mood, affect, and behavior.
  • High-frequency data collection in these studies leads to unavoidable missing data.
  • Existing research on missing data in dynamic structural equation models (DSEM) is limited, often assuming data are missing at random (MAR).

Purpose of the Study:

  • To address the challenge of missing not at random (MNAR) data within dynamic structural equation models (DSEM).
  • To propose and evaluate a novel approach for handling MNAR data in intensive longitudinal studies, particularly for sensitive outcomes.
  • To provide a practical method for researchers using DSEM with complex missing data patterns.

Main Methods:

  • Embedding a Diggle-Kenward-type MNAR model within a DSEM framework.
  • Applying the proposed method to a motivating example of binge eating disorder with self-reported overeating data.
  • Conducting a simulation study to assess the performance of the proposed MNAR model for continuous and binary outcomes.

Main Results:

  • The proposed DSEM approach effectively handles MNAR data, outperforming standard MAR models in relevant scenarios.
  • The method is shown to be applicable and relatively easy to implement in statistical software like Mplus.
  • Simulation results demonstrate the model's utility for both continuous and binary outcome variables.

Conclusions:

  • The proposed DSEM with an embedded MNAR component offers a robust solution for analyzing intensive longitudinal data with missingness.
  • This approach is crucial for accurate modeling of sensitive behaviors where MNAR is likely.
  • Researchers can improve the validity of their findings by adopting this method for handling complex missing data in DSEM.