11: Estimating Obesity's Effect on Chronic Disease Using a Copula Model with Device-Measured Activity

Music City Center 

Analyzing the effect of obesity on chronic disease risk is challenging due to endogeneity, measurement error, and complex dependencies between obesity, physical activity, and health outcomes. Standard statistical methods, such as generalized linear model, often fail to adequately address these issues, leading to biased estimates. To overcome these limitations, we develop a bivariate semi-parametric recursive copula model that flexibly accounts for non-linear relationships and intricate dependency structures. We evaluate the finite sample properties of our approach through simulation studies and apply it to NHANES 2011–2014, incorporating device-measured physical activity to enhance estimation accuracy. Results confirm the robustness of our method and reinforce the causal association between obesity and chronic disease risk. This study highlights the importance of advanced statistical techniques for improving average treatment effect (ATE) in epidemiological research.

Obesity

cardiovascular disease

semi-parametric recursive copula model

endogeneity

physical activity

diabetes 

Biometrics Section