Statistical Methods for Mediation, Confounding and Moderation Analysis Using R and SAS(2022)

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    Third-variable effect (TVE) refers to the intervening effect of a third-variable on the observed relationship between an exposure and an outcome. The thirdvariable effect analysis (TVEA) differentiates the effect from multiple third variables that explain the established exposure-outcome relationship. Depending on whether there is a causal relationship from the exposure to the third variable to the outcome, the third-variable effect can be categorized into two major groups: mediation effect where the causal relationship is assumed, and confounding effect where causal relationship is not presumed. The third variable is called a mediator or confounder respectively. A causal relationship can be established through randomized experiments, while statistical inference can be used to test the hypothesized associations among variables. In terms of the statistical inference on the third-variable effect, the mediation analysis and confounding analysis are equivalent, we call the analysis method the third-variable effect analysis in the book. Furthermore, the moderation effect is another important part of third-variable effect analysis. When the effects of a third-variable vary at different levels of another variable (called moderator), we use moderation analysis to differentiate the third-variable effect at each level of the moderator.


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