Related Experiment Video
Updated: Mar 28, 2026

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
Published on: January 8, 2020
Estimating the Time-Varying Joint Effects of Obesity and Smoking on All-Cause Mortality Using Marginal Structural
Abstract:
Obesity and smoking are independently associated with a higher mortality risk, but previous studies have reported conflicting results about the relationship between these 2 time-varying exposures. Using prospective longitudinal data (1987-2007) from the Atherosclerosis Risk in Communities Study, our objective in the present study was to estimate the joint effects of obesity and smoking on all-cause mortality and investigate whether there were additive or multiplicative interactions. We fit a joint marginal structural Poisson model to account for time-varying confounding affected by prior exposure to obesity and smoking. The incidence rate ratios from the joint model were 2.00 (95% confidence interval (CI): 1.79, 2.24) for the effect of smoking on mortality among nonobese persons, 1.31 (95% CI: 1.13, 1.51) for the effect of obesity on mortality among nonsmokers, and 1.97 (95% CI: 1.73, 2.22) for the joint effect of smoking and obesity on mortality. The negative product term from the exponential model revealed a submultiplicative interaction between obesity and smoking (β = -0.28, 95% CI: -0.45, -0.11; P < 0.001). The relative excess risk of interaction was -0.34 (95% CI: -0.60, -0.07), indicating the presence of subadditive interaction. These results provide important information for epidemiologists, clinicians, and public health practitioners about the harmful impact of smoking and obesity.
More Related Videos
Related Concept Videos
Bias in Epidemiological Studies
Clearance Models: Noncompartmental Models
The noncompartmental approach capitalizes on extensive sampling data, correlating the volume of distribution to systemic exposure and the administered dosage. This method enables...
Statistical Methods for Analyzing Epidemiological Data
Obesity
Parametric Survival Analysis: Weibull and Exponential Methods
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...
Mechanistic Models: Compartment Models in Individual and Population Analysis

