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Decomposition of the Total Effect in the Presence of Multiple Mediators and Interactions.

ABSTRACT: Mediation analysis allows decomposing a total effect into a direct effect of the exposure on the outcome and an indirect effect operating through a number of possible hypothesized pathways. Recent studies have provided formal definitions of direct and indirect effects when multiple mediators are of interest and have described parametric and semiparametric methods for their estimation. Investigating direct and indirect effects with multiple mediators, however, can be challenging in the presence of multiple exposure-mediator and mediator-mediator interactions. In this paper we derive a decomposition of the total effect that unifies mediation and interaction when multiple mediators are present. We illustrate the properties of the proposed framework in a secondary analysis of a pragmatic trial for the treatment of schizophrenia. The decomposition is employed to investigate the interplay of side effects and psychiatric symptoms in explaining the effect of antipsychotic medication on quality of life in schizophrenia patients. Our result offers a valuable tool to identify the proportions of total effect due to mediation and interaction when more than one mediator is present, providing the finest decomposition of the total effect that unifies multiple mediators and interactions.

SUBMITTER: Bellavia A

PROVIDER: S-EPMC6692646 | biostudies-literature | 2018 Jun

REPOSITORIES: biostudies-literature

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Decomposition of the Total Effect in the Presence of Multiple Mediators and Interactions.

Bellavia Andrea A Valeri Linda L

American journal of epidemiology 20180601 6

Mediation analysis allows decomposing a total effect into a direct effect of the exposure on the outcome and an indirect effect operating through a number of possible hypothesized pathways. Recent studies have provided formal definitions of direct and indirect effects when multiple mediators are of interest and have described parametric and semiparametric methods for their estimation. Investigating direct and indirect effects with multiple mediators, however, can be challenging in the presence o ...[more]

PMID: 29140421

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Project description:OBJECTIVES:In observational studies, epidemiologists often attempt to estimate the total effect of an exposure on an outcome of interest. However, when the underlying diagram is unknown and limited knowledge is available, dissecting bias performances is essential to estimating the total effect of an exposure on an outcome when mistakenly adjusting for mediators under logistic regression. Through simulation, we focused on six causal diagrams concerning different roles of mediators. Sensitivity analysis was conducted to assess the bias performances of varying across exposure-mediator effects and mediator-outcome effects when adjusting for the mediator. SETTING:Based on the causal relationships in the real world, we compared the biases of varying across the effects of exposure-mediator with those of varying across the effects of mediator-outcome when adjusting for the mediator. The magnitude of the bias was defined by the difference between the estimated effect (using logistic regression) and the total effect of the exposure on the outcome. RESULTS:In four scenarios (a single mediator, two series mediators, two independent parallel mediators or two correlated parallel mediators), the biases of varying across the effects of exposure-mediator were greater than those of varying across the effects of mediator-outcome when adjusting for the mediator. In contrast, in two other scenarios (a single mediator or two independent parallel mediators in the presence of unobserved confounders), the biases of varying across the effects of exposure-mediator were less than those of varying across the effects of mediator-outcome when adjusting for the mediator. CONCLUSIONS:The biases were more sensitive to the variation of effects of exposure-mediator than the effects of mediator-outcome when adjusting for the mediator in the absence of unobserved confounders, while the biases were more sensitive to the variation of effects of mediator-outcome than those of exposure-mediator in the presence of an unobserved confounder.

Project description:Many existing cohort studies initially designed to investigate disease risk as a function of environmental exposures have collected genomic data in recent years with the objective of testing for gene-environment interaction (G × E) effects. In environmental epidemiology, interest in G × E arises primarily after a significant effect of the environmental exposure has been documented. Cohort studies often collect rich exposure data; as a result, assessing G × E effects in the presence of multiple exposure markers further increases the burden of multiple testing, an issue already present in both genetic and environment health studies. Latent variable (LV) models have been used in environmental epidemiology to reduce dimensionality of the exposure data, gain power by reducing multiplicity issues via condensing exposure data, and avoid collinearity problems due to presence of multiple correlated exposures. We extend the LV framework to characterize gene-environment interaction in presence of multiple correlated exposures and genotype categories. Further, similar to what has been done in case-control G × E studies, we use the assumption of gene-environment (G-E) independence to boost the power of tests for interaction. The consequences of making this assumption, or the issue of how to explicitly model G-E association has not been previously investigated in LV models. We postulate a hierarchy of assumptions about the LV model regarding the different forms of G-E dependence and show that making such assumptions may influence inferential results on the G, E, and G × E parameters. We implement a class of shrinkage estimators to data adaptively trade-off between the most restrictive to most flexible form of G-E dependence assumption and note that such class of compromise estimators can serve as a benchmark of model adequacy in LV models. We demonstrate the methods with an example from the Early Life Exposures in Mexico City to Neuro-Toxicants Study of lead exposure, iron metabolism genes, and birth weight.

Dataset Information

Decomposition of the Total Effect in the Presence of Multiple Mediators and Interactions.

Publications

Decomposition of the Total Effect in the Presence of Multiple Mediators and Interactions.

Similar Datasets

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