A statistical method frequently used in clinical research in recent years: Causal mediation analysis

Abstract

In recent years, in addition to treatment and outcome variables, mediator variables have been also included in the statistical models used in clinical studies. These mediating variables are usually used as confounding factors in studies and are attempted to be controlled using multivariate regression models according to the type of outcome variable. However, the confounding factors in the models are insufficient to reveal the variable that plays a mediating role between treatment and outcome variables. If researchers have prior knowledge that another explanatory variable has a direct or indirect effect on the outcome, they may want to reflect this in the model and reveal the causal effect indirectly. In this case, there is a mediator variable that indirectly transfers the causal effect. With the help of causal mediation analysis in observational and randomized-controlled trials, it becomes possible to generate evidence about situations in which interventions and exposures may affect health outcomes. The first study on causal mediation analysis, which emerged with this need, was on the concept of “mediated or indirect effects in pathway models of the inheritance of skin color in guinea pigs” introduced by Sewall Wright in 1920. Subsequently, Hyman in 1955 and Lazarsfeld in 1955 provided the original definitions of how a third variable influences the relationship between two variables through a series of statistical tests that were later translated into decomposition of effects by Alwin and Hauser in 1975, mediation in psychology by James and Brett in 1984 and Baron and Kenny in 1986, and evaluation by Judd and Kenny in 1981. The extremely rapid innovations in mediation analysis in recent years, particularly in the last three or four decades, have played an important role in the development of the analysis and a better understanding of its basis