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Bayesian continuous-time hidden Markov models with covariate selection for intensive longitudinal data with
Mingrui Liang1, Matthew D Koslovsky2, Emily T Hébert3
1Department of Statistics, Rice University.
Psychological Methods
|December 20, 2021
Summary
This study introduces a Bayesian hidden Markov model to analyze ecological momentary assessment data, accounting for misreporting and improving accuracy in behavioral research. The model enhances understanding of risk factors in real-time data collection.
Area of Science:
- Behavioral Science
- Statistical Modeling
- Psychological Measurement
Background:
- Ecological momentary assessment (EMA) provides real-time data but is prone to self-report biases like measurement error and social desirability bias.
- Traditional analyses may not adequately address these biases, potentially impacting the accuracy of identified risk factors.
- Accurate analysis of EMA data is crucial for understanding behavior change and intervention effectiveness.
Purpose of the Study:
- To develop and validate a Bayesian hidden Markov model (BHMM) capable of simultaneously identifying risk factors for state transitions and potential misreporting in EMA data.
- To assess the impact of measurement error on variable selection and estimation accuracy using simulated data.
- To apply the BHMM to real-world EMA data from a smoking cessation trial.
Main Methods:
- Development of a Bayesian hidden Markov model incorporating latent states and measurement error.
- Simulation studies to evaluate model performance under varying conditions of measurement error.
- Application of the BHMM to smartphone-based EMA data from a randomized controlled trial on smoking abstinence.
Main Results:
- Simulations demonstrated that ignoring measurement error can lead to inaccurate variable selection and estimation.
- The BHMM successfully identified risk factors associated with state transitions and potential misreporting in the smoking cessation trial data.
- The model provides a more robust approach to analyzing complex EMA data compared to methods that ignore measurement error.
Conclusions:
- The proposed Bayesian hidden Markov model offers a powerful tool for analyzing intensive longitudinal data, effectively addressing biases inherent in self-reported measures.
- Accounting for measurement error is essential for accurate identification of risk factors and reliable conclusions from EMA studies.
- This methodology can enhance the validity of findings in behavioral and psychological research utilizing EMA.
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