G-Computation to Causal Mediation Analysis With Sequential Multiple Mediators—Investigating the Vulnerable Time Window of HBV Activity for the Mechanism of HCV Induced Hepatocellular Carcinoma

Regression-based approaches are widely used in causal mediation analysis. The presence of multiple mediators, however, increases the complexity and difficulty of mediation analysis. In such cases, regression-based approaches cannot efficiently address estimation issues. Hence, a flexible approach to mediation analysis is needed. Therefore, we developed a method for using g-computation algorithm to conduct causal mediation analysis in the presence of multiple ordered mediators. Compared to regression-based approaches, the proposed simulation-based approach increases flexibility in the choice of models and increases the range of the outcome scale. The Taiwanese Cohort Study dataset was used to evaluate the efficacy of the proposed approach for investigating the mediating role of early and late HBV viral load in the effect of HCV infection on hepatocellular carcinoma (HCC) in HBV seropositive patients (n = 2,878; HCV carrier n = 123). Our results indicated that early HBV viral load had a negative mediating role in HCV-induced HCC. Additionally, early exposure to a low HBV viral load affected HCC through a lag effect on HCC incidence [OR = 0.873, 95% CI = (0.853, 0.893)], and the effect of early exposure to a low HBV viral load on HCC incidence was slightly larger than that of a persistently low viral load on HCC incidence [OR = 0.918, 95% CI = (0.896, 0.941)].

R package regmedint: extension of regression-based causal mediation analysis with effect measure modification by covariates

This is the supplementary document of R package regmedint that implements the extension of the regression-based causal mediation analysis first proposed by Valeri and VanderWeele (2013, 2015). It supports including effect measure modification by covariates (treatment-covariate and mediator-covariate product terms in mediator and outcome regression models), and also accommodates the original SAS macro and PROC CAUSALMED procedure in SAS when there is no effect measure modification.

Anxiety, Depression, Perceived Executive Function and Sugar Sweetened Beverages: A Causal Mediation Analysis in Mexican Young Adults

Background: There is the notion that dysregulation of executive function (EF), which disrupts behavior and cognitive processes, may explain the emotional impairment which leads to increasing sugar sweetened beverages (SSBs) intake. We aimed to test whether anxiety and depression mediate the association between self-reported problems in EF and sugar sweetened beverage intake in Mexican adults between 18-40 years. Methods: An open survey advertised as a “mental health and sugar sweetened beverages study” was conducted, participants were recruited through Facebook ads Males and females, between 18-40 years, able to read and write, and currently residing in Mexico were included. We analyzed data using causal mediation analysis with latent variables using a structural equation modelling framework. Results: Significant indirect effects were found for somatic symptoms of depression (β = 6.601, SE = 2.657, p<.05) and anxiety (β = .679, SE = .334, p<.05). In depression model no significant direct effects of EF were found in the depression model, however they were found in the anxiety model (β = 1.335 SE = .566, p<.05). Conclusions: Somatic symptoms of depression were a total mediator, and anxiety a partial mediator. The results suggests that EF increased the probability of emotional symptoms, which led to a higher consumption of SBBs

Sensitivity analysis for unobserved confounding in causal mediation analysis allowing for effect modification, censoring and truncation

Causal mediation analysis is used to decompose the total effect of an exposure on an outcome into an indirect effect, taking the path through an intermediate variable, and a direct effect. To estimate these effects, strong assumptions are made about unconfoundedness of the relationships between the exposure, mediator and outcome. These assumptions are difficult to verify in a given situation and therefore a mediation analysis should be complemented with a sensitivity analysis to assess the possible impact of violations. In this paper we present a method for sensitivity analysis to not only unobserved mediator-outcome confounding, which has largely been the focus of previous literature, but also unobserved confounding involving the exposure. The setting is estimation of natural direct and indirect effects based on parametric regression models. We present results for combinations of binary and continuous mediators and outcomes and extend the sensitivity analysis for mediator-outcome confounding to cases where the continuous outcome variable is censored or truncated. The proposed methods perform well also in the presence of interactions between the exposure, mediator and observed confounders, allowing for modeling flexibility as well as exploration of effect modification. The performance of the method is illustrated through simulations and an empirical example.