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Related Experiment Video

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A Bayesian transition model for missing longitudinal binary outcomes and an application to a smoking cessation study.

Li Li1, Ji-Hyun Lee2, Steven K Sutton3

  • 1Department of Mathematics and Statistics, University of New Mexico, Albuquerque, NM, USA.

Statistical Modelling
|April 15, 2021
PubMed
Summary

This study introduces a Bayesian method to model dynamic smoking status transitions and impute missing data in cessation trials. It helps determine intervention effectiveness in maintaining abstinence or aiding quitting.

Keywords:
Bayesian methodgeneralized linear mixed modelmissing valuessmoking cessationtransition model

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Area of Science:

  • Biostatistics
  • Public Health
  • Behavioral Science

Background:

  • Smoking cessation studies frequently yield discrete smoking status data with varying missingness.
  • Smoking status is dynamic, involving transitions between smoking and abstinence.

Purpose of the Study:

  • To model dynamic changes in smoking status during interventions.
  • To assess how interventions and covariates influence transitions between smoking and abstinence.
  • To develop a robust method for handling missing data in smoking cessation trials.

Main Methods:

  • A Bayesian approach to fit a transition model for smoking status.
  • Imputation of missing outcomes using a logistic model accommodating missing at random (MAR) and missing not at random (MNAR) mechanisms.
  • Utilizing posterior predictive checking and log pseudo marginal likelihood (LPML) for model assessment and comparison.

Main Results:

  • The proposed Bayesian method effectively models smoking status transitions.
  • The approach successfully imputes missing data, accounting for MAR and MNAR mechanisms.
  • Simulation studies and a randomized controlled trial demonstrate the method's performance.

Conclusions:

  • The Bayesian transition model provides a powerful tool for analyzing dynamic smoking status in cessation studies.
  • This method offers improved handling of missing data, leading to more reliable intervention effect estimates.
  • The approach aids in understanding factors influencing smoking cessation and relapse.