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Mediation Analysis.  

Edward H. Livingston

in AMA Manual of Style: A Guide for Authors and Editors (11th edn)

Print Publication Year: 
Feb 2020
Published Online: 
Feb 2020
DOI: 
10.1093/jama/9780190246556.003.0019.022.2125
Item type: 
section
In studies that use mediation analysis , the relationship between intervention and outcome is partitioned into indirect and direct effects or associations. These relationships are often shown in a diagram89 (see Figure 19.3-2). Mediation analysis can estimate indirect and direct relationships and the proportion mediated, a statistical measure estimating how much of the total intervention works through a particular mediator.The explicit objective of mediation analyses is to demonstrate potential causal relationships; however, this may not be possible and requires that specific assumptions be met. In a mediation analysis, the intervention-outcome, intervention-mediator, and mediator-outcome relationships must be unconfounded to permit valid causal inferences. In a randomized trial, participants are randomly assigned to intervention groups, so the intervention-outcome and intervention-mediator effects can be assumed to be unconfounded. However, trial participants are not usually randomly assigned to receive or not receive the mediator, so the mediator-outcome relationship may be confounded, even in randomized trials. To overcome this potential source of bias, investigators can control for known confounders of the mediator-outcome effect by using techniques such as regression adjustment. However, unmeasured confounding may still introduce bias even if known confounders have been adjusted for. Sensitivity analyses should be used to assess the potential bias caused by unmeasured confounding in mediation analyses. The risk of confounding in mediation analyses is greater in observational studies than in randomized trials, and in these cases, caution is required when interpreting findings and is best reported as interpreting estimates of indirect and direct associations....

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