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Generalized estimating equations for modeling cluster randomized trial data on smoking cessation among tuberculosis
Vasantha Mahalingam1, Ratnakar Singh1, Ramesh Kumar Santhanakrishnan2
1Department of Statistics, ICMR-National Institute for Research in Tuberculosis, Chennai, Tamil Nadu, India.
Plos One
|October 10, 2025
Summary
Generalized Estimating Equations (GEE) analysis of smoking cessation in tuberculosis (TB) patients showed integrated interventions improve outcomes. This longitudinal study confirms GEE
Area of Science:
- Public Health
- Epidemiology
- Biostatistics
Background:
- Longitudinal analysis of smoking cessation in tuberculosis (TB) patients is understudied, particularly within cluster randomized trials.
- Generalized Estimating Equations (GEE) offer a robust method for analyzing repeated measures and cluster-level effects in such populations.
Purpose of the Study:
- To apply GEE for longitudinal analysis of smoking cessation outcomes among TB patients.
- To identify factors associated with smoking cessation in this cohort.
- To evaluate the impact of integrated smoking cessation interventions within TB treatment.
Main Methods:
- Utilized GEE modeling to account for repeated measures and intra-cluster correlation.
- Analyzed data from 375 TB patients (smokers) undergoing treatment in Kanchipuram and Villupuram districts (2013-2016).
- Employed a cluster randomized trial framework.
Main Results:
- GEE provided robust, population-averaged estimates.
- The analysis confirmed the sustained impact of integrated interventions on smoking cessation.
- Factors associated with smoking cessation were identified.
Conclusions:
- Integrated smoking cessation interventions alongside TB treatment positively impact cessation outcomes.
- GEE is a suitable statistical approach for analyzing longitudinal smoking cessation data in cluster randomized trials involving TB patients.
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