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

ABSTRACT: Smoking cessation intervention studies often produce data on smoking status at discrete follow-up assessments, often with missing data in different amounts at each assessment. Smoking status in these studies is a dynamic process with individuals transitioning from smoking to abstinent, as well as abstinent to smoking, at different times during the intervention. Directly assessing transitions provides an opportunity to answer important questions like 'Does the proposed intervention help smokers remain abstinent or quit smoking more effectively than other interventions?' In this article, we model changes in smoking status and examine how interventions and other covariates affect the transitions. We propose a Bayesian approach for fitting the transition model to the observed data and impute m

SUBMITTER: Li L 

PROVIDER: S-EPMC8043653 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature