Instrumental-Variables Simultaneous Equations Model of Physical Activity and Body Mass Index

Abstract

We used full-system-estimation instrumental-variables simultaneous equations modeling (IV-SEM) to examine physical activity relative to body mass index (BMI; weight (kg)/height (m)2 ) using 25 years of data (1985/1986 to 2010/2011) from the Coronary Artery Risk Development in Young Adults (CARDIA) Study (n = 5,115; ages 18-30 years at enrollment). Neighborhood environment and sociodemographic instruments were used to characterize physical activity, fast-food consumption, smoking, alcohol consumption, marriage, and childbearing (women) and to predict BMI using semiparametric full-information maximum likelihood estimation to control for unobserved timeinvariant and time-varying residual confounding and differential measurement error through model-derived discrete random effects. Comparing robust-variance ordinary least squares, random-effects regression, fixed-effects regression, single-equation-estimation IV-SEM, and full-system-estimation IV-SEM, estimates from random-and fixed-effects models and the full-system-estimation IV-SEM were unexpectedly similar, despite the lack of control for residual confounding with the random-effects estimator. Ordinary least squares tended to overstate the significance of health behaviors in BMI, while results from single-equation-estimation IV-SEM were notably different, revealing the impact of weak instruments in standard instrumental-variable methods. Our robust findings for fixed effects (which does not require instruments but has a high cost in lost degrees of freedom) and full-system-estimation IV-SEM (vs. standard IV-SEM) demonstrate potential for a full-system-estimation IV-SEM method even with weak instruments. body mass index; endogeneity; epidemiologic methods; fixed effects; health behaviors; instrumental variables; semiparametric methods; simultaneous equations Abbreviations: BMI, body mass index; CARDIA, Coronary Artery Risk Development in Young Adults; CI, confidence interval; FIML, full-information maximum likelihood; GMM, generalized method of moments; IV, instrumental variable; IV-SEM, instrumentalvariables simultaneous equations model/modeling; OLS, ordinary least squares.

Does unmeasured confounding influence associations between the retail food environment and body mass index over time? The Coronary Artery Risk Development in Young Adults (CARDIA) study

“…presence of weak instruments. 41 We also acknowledge that the retail food environment is only one risk factor for weight gain, and additional risk factors should be considered in future research, including factors related to food availability and prices in school, professional and recreational environments (i.e. not retail food outlets).…”

Social capital effects on resilience to food insecurity: Evidence from Kyrgyzstan

“…In this case, Maydeu‐Olivares et al (2019) have confirmed that both 2SLS and IV‐SEM provide accurate coverage rates in the presence of endogeneity if the sample size is bigger than 500 observations. As long as 2SLS is practically susceptible under the weak instrument assumption, IV‐SEM allowing nonlinear estimation is robust (Meyer et al, 2016). In addition to the 2SLS and IV‐SEM models, a two‐stage regression model was regressed manually with a bootstrapping method.…”