Statistical grand rounds: understanding the mechanism: mediation analysis in randomized and nonrandomized studies

In comparative clinical studies, a common goal is to assess whether an exposure, or intervention, affects the outcome of interest. However, just as important is to understand the mechanism(s) for how the intervention affects outcome. For example, if preoperative anemia was shown to increase the risk of postoperative complications by 15%, it would be important to quantify how much of that effect was due to patients receiving intraoperative transfusions. Mediation analysis attempts to quantify how much, if any, of the effect of an intervention on outcome goes though prespecified mediator, or "mechanism" variable(s), that is, variables sitting on the causal pathway between exposure and outcome. Effects of an exposure on outcome can thus be divided into direct and indirect, or mediated, effects. Mediation is claimed when 2 conditions are true: the exposure affects the mediator and the mediator (adjusting for the exposure) affects the outcome. Understanding how an intervention affects outcome can validate or invalidate one's original hypothesis and also facilitate further research to modify the responsible factors, and thus improve patient outcome. We discuss the proper design and analysis of studies investigating mediation, including the importance of distinguishing mediator variables from confounding variables, the challenge of identifying potential mediators when the exposure is chronic versus acute, and the requirements for claiming mediation. Simple designs are considered, as well as those containing multiple mediators, multiple outcomes, and mixed data types. Methods are illustrated with data collected by the National Surgical Quality Improvement Project (NSQIP) and utilized in a companion paper which assessed the effects of preoperative anemic status on postoperative outcomes.