Literature DB >> 25309023

Controlled direct and mediated effects: definition, identification and bounds.

Tyler J VanderWeele1.   

Abstract

Results are given which provide bounds for controlled direct effects when the no-unmeasured-confounding assumptions required for the identification of these effects do not hold. Previous results concerning bounds for controlled direct effects rely on monotonicity relationships between the treatment, mediator and the outcome themselves; the results presented in this paper instead assume that monotonicity relationships hold between the unmeasured confounding variable or variables and the treatment, mediator and outcome. Whereas prior results give bounds that contain the null hypothesis of no direct effect, the results presented here will in many instances yield bounds that do not contain the null hypothesis of no direct effect. For contexts in which a set of variables intercepts all paths between a treatment and an outcome, it is possible to provide a definition for a controlled mediated effect. We discuss the identification of these controlled mediated effects; the bounds for controlled direct effects are applicable also to controlled mediated effects. An example is given to illustrate how the results in the paper can be used to draw inferences about direct and mediated effects in the presence of unmeasured confounding variables.

Entities:  

Keywords:  Bounds; causal inference; direct and indirect effects; mediation; unmeasured confounding

Year:  2011        PMID: 25309023      PMCID: PMC4193506          DOI: 10.1111/j.1467-9469.2010.00722.x

Source DB:  PubMed          Journal:  Scand Stat Theory Appl        ISSN: 0303-6898            Impact factor:   1.396


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5.  The sign of the bias of unmeasured confounding.

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10.  A further critique of the analytic strategy of adjusting for covariates to identify biologic mediation.

Authors:  Jay S Kaufman; Richard F Maclehose; Sol Kaufman
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8.  Parametric-Regression-Based Causal Mediation Analysis of Binary Outcomes and Binary Mediators: Moving Beyond the Rareness or Commonness of the Outcome.

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9.  Education achievement and type 2 diabetes-what mediates the relationship in older adults? Data from the ESTHER study: a population-based cohort study.

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10.  Analysis of Longitudinal Studies With Repeated Outcome Measures: Adjusting for Time-Dependent Confounding Using Conventional Methods.

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