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Challenges and novel approaches for investigating molecular mediation

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ABSTRACT

Understanding mediation is useful for identifying intermediates lying between an exposure and an outcome which, when intervened upon, will block (some or all of) the causal pathway between the exposure and outcome. Mediation approaches used in conventional epidemiology have been adapted to understanding the role of molecular intermediates in situations of high-dimensional omics data with varying degrees of success. In particular, the limitations of observational epidemiological study including confounding, reverse causation and measurement error can afflict conventional mediation approaches and may lead to incorrect conclusions regarding causal effects. Solutions to analysing mediation which overcome these problems include the use of instrumental variable methods such as Mendelian randomization, which may be applied to evaluate causality in increasingly complex networks of omics data.

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Related in: MedlinePlus

Distinguishing mediation from reverse causation and confounding In a situation of mediation, the effect of the exposure (E) on an outcome (Y) is mediated through an intermediate (M). In a situation of reverse cause, E influences Y which then has an effect on M. In a situation of common cause (confounding), E has an independent effect on both M and Y, so inducing a spurious association between M and Y.
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ddw197-F3: Distinguishing mediation from reverse causation and confounding In a situation of mediation, the effect of the exposure (E) on an outcome (Y) is mediated through an intermediate (M). In a situation of reverse cause, E influences Y which then has an effect on M. In a situation of common cause (confounding), E has an independent effect on both M and Y, so inducing a spurious association between M and Y.

Mentions: One of the most widely cited approaches for evaluating mediation in an epidemiological setting is that originally outlined by Baron and Kenny (19). This regression-based approach may be applied to distinguish a mediated effect of the exposure (E) on an outcome (Y) through an intermediate (M) from both a consequential (reverse cause) effect and a common cause (confounding) effect (Fig. 3), through the application of four tests:Figure 3.


Challenges and novel approaches for investigating molecular mediation
Distinguishing mediation from reverse causation and confounding In a situation of mediation, the effect of the exposure (E) on an outcome (Y) is mediated through an intermediate (M). In a situation of reverse cause, E influences Y which then has an effect on M. In a situation of common cause (confounding), E has an independent effect on both M and Y, so inducing a spurious association between M and Y.
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC5036871&req=5

ddw197-F3: Distinguishing mediation from reverse causation and confounding In a situation of mediation, the effect of the exposure (E) on an outcome (Y) is mediated through an intermediate (M). In a situation of reverse cause, E influences Y which then has an effect on M. In a situation of common cause (confounding), E has an independent effect on both M and Y, so inducing a spurious association between M and Y.
Mentions: One of the most widely cited approaches for evaluating mediation in an epidemiological setting is that originally outlined by Baron and Kenny (19). This regression-based approach may be applied to distinguish a mediated effect of the exposure (E) on an outcome (Y) through an intermediate (M) from both a consequential (reverse cause) effect and a common cause (confounding) effect (Fig. 3), through the application of four tests:Figure 3.

View Article: PubMed Central - PubMed

ABSTRACT

Understanding mediation is useful for identifying intermediates lying between an exposure and an outcome which, when intervened upon, will block (some or all of) the causal pathway between the exposure and outcome. Mediation approaches used in conventional epidemiology have been adapted to understanding the role of molecular intermediates in situations of high-dimensional omics data with varying degrees of success. In particular, the limitations of observational epidemiological study including confounding, reverse causation and measurement error can afflict conventional mediation approaches and may lead to incorrect conclusions regarding causal effects. Solutions to analysing mediation which overcome these problems include the use of instrumental variable methods such as Mendelian randomization, which may be applied to evaluate causality in increasingly complex networks of omics data.

No MeSH data available.


Related in: MedlinePlus