title: Semiparametric Estimation of the Impacts of Longitudinal Interventions on Adolescent Obesity using Targeted Maximum-Likelihood: Accessible Estimation with the ltmle Package.
abstract: While child and adolescent obesity is a serious public health concern, few studies have utilized parameters based on the causal inference literature to examine the potential impacts of early intervention. The purpose of this analysis was to estimate the causal effects of early interventions to improve physical activity and diet during adolescence on body mass index (BMI), a measure of adiposity, using improved techniques. The most widespread statistical method in studies of child and adolescent obesity is multi-variable regression, with the parameter of interest being the coefficient on the variable of interest. This approach does not appropriately adjust for time-dependent confounding, and the modeling assumptions may not always be met. An alternative parameter to estimate is one motivated by the causal inference literature, which can be interpreted as the mean change in the outcome under interventions to set the exposure of interest. The underlying data-generating distribution, upon which the estimator is based, can be estimated via a parametric or semi-parametric approach. Using data from the National Heart, Lung, and Blood Institute Growth and Health Study, a 10-year prospective cohort study of adolescent girls, we estimated the longitudinal impact of physical activity and diet interventions on 10-year BMI z-scores via a parameter motivated by the causal inference literature, using both parametric and semi-parametric estimation approaches. The parameters of interest were estimated with a recently released R package, ltmle, for estimating means based upon general longitudinal treatment regimes. We found that early, sustained intervention on total calories had a greater impact than a physical activity intervention or non-sustained interventions. Multivariable linear regression yielded inflated effect estimates compared to estimates based on targeted maximum-likelihood estimation and data-adaptive super learning. Our analysis demonstrates that sophisticated, optimal semiparametric estimation of longitudinal treatment-specific means via ltmle provides an incredibly powerful, yet easy-to-use tool, removing impediments for putting theory into practice.
authors.affiliation: University of California - Berkeley, Berkeley, CA 94704, USA.
authors.forename: Anna L
authors.initials: AL
authors.lastname: Decker
authors.affiliation: Division of Biostatistics, University of California - Berkeley, Berkeley, CA, USA.
authors.forename: Alan
authors.initials: A
authors.lastname: Hubbard
authors.affiliation: University of California - Los Angeles, Los Angeles, CA, USA.
authors.forename: Catherine M
authors.initials: CM
authors.lastname: Crespi
authors.affiliation: University of Washington - Seattle, Seattle, WA, 98195, USA.
authors.forename: Edmund Y W
authors.initials: EY
authors.lastname: Seto
authors.affiliation: University of California - Los Angeles, Los Angeles, CA, USA.
authors.forename: May C
authors.initials: MC
authors.lastname: Wang
keywords: causal inference; longitudinal data; obesity