The impact of adjusting for pure predictors of exposure, mediator, and outcome on the variance of natural direct and indirect effect estimators

Prospective association of screen time with binge-eating disorder among adolescents in the United States: The mediating role of depression

OBJECTIVE

Screen time has been reported to be associated with binge-eating disorder (BED) among adolescents in the US; however, potential mediators remain unclear. This study aimed to evaluate depression symptoms as a mediator of the prospective association between screen time and BED.

METHOD

We utilized data from 9465 children (aged 9-11 years at baseline) from the Adolescent Brain Cognitive Development (ABCD) study (2016-2021). A generalized structural equation model was used to examine the prospective association between average daily screen time at baseline and BED at year 2, adjusting for baseline BED diagnosis, and other potential covariates (e.g., age, sex, and income). Mediation was examined using bias-corrected (BC) 95% confidence intervals for the indirect effect of baseline screen time on year 2 BED through depression symptoms (change from baseline to year 1).

RESULTS

One hundred and one participants (42.7% male, 49.4% racial/ethnic minority) met the criteria for BED in year 2. Participants were 9.9 years of age on average at baseline, 51.3% identified as male, and 43.1% identified as a racial/ethnic minority. Adjusting for covariates, screen time was prospectively associated with BED (OR = 1.09, 95% CI [1.03, 1.14], p = .005). Depression symptoms (B = .19, BC 95% CI [0.10, 0.28]) partially mediated (9.2%) the prospective association between screen time and BED.

DISCUSSION

Among US adolescents, higher baseline screen time was prospectively associated with BED diagnosis at year 2, and this relationship was partially mediated by increased depression symptoms. Preventive approaches targeting high screen use may have utility for reducing BED risk among adolescents.

PUBLIC SIGNIFICANCE

Among U.S. adolescents, higher screen time was prospectively associated with the incidence of BED. This association was partially mediated by the change in depressive symptoms. Preventive approaches targeting high screen use may have utility for reducing BED risk among adolescents.

An exact regression-based approach for the estimation of natural direct and indirect effects with a binary outcome and a continuous mediator

In the causal mediation framework, a number of parametric regression-based approaches have been introduced in recent years for estimating natural direct and indirect effects for a binary outcome in an exact manner, without invoking simplifying assumptions based on the rareness or commonness of the outcome. However, most of these works have focused on a binary mediator. In this article, we aim at a continuous mediator and introduce an exact approach for the estimation of natural effects on the odds ratio, risk ratio, and risk difference scales. Our approach relies on logistic and linear models for the outcome and mediator, respectively, and uses numerical integration to calculate the nested counterfactual probabilities underlying the definition of natural effects. Formulas for the delta method standard errors for all effects estimators are provided. The performance of our proposed exact estimators was evaluated in simulation studies that featured scenarios with different levels of outcome rareness/commonness, including a marginally but not conditionally rare outcome scenario. Furthermore, we evaluated the merit of Firth's penalization to mitigate the bias in the logistic regression coefficients estimators for the smallest outcome prevalences and sample sizes investigated. Using a SAS macro provided, we implemented our approach to assess the effect of placental abruption on low birth weight mediated by gestational age. We found that our exact natural effects estimators worked properly in both simulated and real data applications.

The impact of adjusting for pure predictors of exposure, mediator, and outcome on the variance of natural direct and indirect effect estimators

It is now well established that adjusting for pure predictors of the outcome, in addition to confounders, allows unbiased estimation of the total exposure effect on an outcome with generally reduced standard errors (SEs). However, no analogous results have been derived for mediation analysis. Considering the simplest linear regression setting and the ordinary least square estimator, we obtained theoretical results showing that adjusting for pure predictors of the outcome, in addition to confounders, allows unbiased estimation of the natural indirect effect (NIE) and the natural direct effect (NDE) on the difference scale with reduced SEs. Adjusting for pure predictors of the mediator increases the SE of the NDE's estimator, but may increase or decrease the variance of the NIE's estimator. Adjusting for pure predictors of the exposure increases the variance of estimators of the NIE and NDE. Simulation studies were used to confirm and extend these results to the case where the mediator or the outcome is binary. Additional simulations were conducted to explore scenarios featuring an exposure-mediator interaction as well as the relative risk and odds ratio scales for the case of binary mediator and outcome. Both a regression approach and an inverse probability weighting approach were considered in the simulation study. A real-data illustration employing data from the Canadian Study of Health and Aging is provided. This analysis is concerned with the mediating effect of vitamin D in the effect of physical activity on dementia and its results are overall consistent with the theoretical and empirical findings.