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Latent trait shared-parameter mixed models for missing ecological momentary assessment data.

John F Cursio1, Robin J Mermelstein2, Donald Hedeker1

  • 1Department of Public Health Sciences, Biological Sciences Division, The University of Chicago, Chicago, Illinois.

Statistics in Medicine
|October 16, 2018
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Summary
This summary is machine-generated.

New statistical models address missing data in ecological momentary assessment (EMA) by treating non-responses as a latent trait. This approach improves analysis of intermittent longitudinal data, offering better accuracy than standard methods.

Keywords:
ecological momentary assessmentintermittent missing datalatent traitlongitudinal datashared-parameter model

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Area of Science:

  • Statistics
  • Psychometrics
  • Longitudinal Data Analysis

Background:

  • Ecological Momentary Assessment (EMA) collects real-time data, but missing responses are common due to intermittent data collection.
  • Item response theory (IRT) and mixed models are standard for longitudinal data, but handling missing EMA data requires advanced techniques.
  • Existing methods for missing data, like missing at random (MAR) models, may not fully capture the complexities of intermittent EMA data.

Purpose of the Study:

  • To develop and present novel latent trait shared-parameter mixed models specifically designed for analyzing EMA data with missing values.
  • To model the missingness mechanism itself as a latent trait representing the propensity to respond.
  • To provide a more accurate and efficient analytical framework for intermittent longitudinal data common in EMA studies.

Main Methods:

  • Development of one- and two-parameter latent trait shared-parameter mixed models.
  • Integration of item response theory to define a latent trait for missing responses.
  • Joint modeling of the latent trait with bivariate longitudinal outcomes.
  • Application to a large dataset (over 10,300 observations) from an EMA study on high school students' affect.
  • Comparison with a standard missing at random (MAR) mixed model and a simulation study.

Main Results:

  • The latent trait representing missingness was a significant predictor of both positive and negative affect outcomes in the EMA data.
  • The proposed latent trait shared-parameter mixed models demonstrated lower bias and increased efficiency compared to the standard MAR mixed model in simulation studies.
  • The models effectively handle various missing response patterns inherent in intermittent EMA data.

Conclusions:

  • Latent trait shared-parameter mixed models offer a robust and statistically sound approach for analyzing EMA data with missing values.
  • Modeling missingness as a latent trait provides valuable insights into response behaviors and improves the accuracy of longitudinal outcome analysis.
  • These novel models represent a significant advancement for researchers dealing with intermittent longitudinal data in psychological and other scientific fields.