Mini Review
New Direction: Mediation Analysis in Perinatal Epidemiological Studies
Gary Kong1 and Qun Miao2*
1Faculty of Arts and Science, Queen’s University, Canada
2CHEO Research Institute, Canada
Qun Miao, CHEO Research Institute, Canada
Received Date: January 28, 2020; Published Date: February 19, 2020
Abstract
In traditional epidemiology, a multivariable regression model is often applied to evaluate the relationship between an exposure and an outcome while adjusting for confounders and taking into account of interactions. In 1992, Robins and Greenland introduced the mediator counterfactual framework concept, which has helped researchers disentangle the complex causal pathway between exposure and outcome. In the past one decade, mediation analysis has been used in various epidemiological studies, including reproductive and perinatal research. This analysis method has been incorporated into various statistical software packages, including the CAUSALMED procedure in SAS, among various others. Mediation analysis is not only used to explain causal relationships between exposure, mediator, interaction and outcome but also provide solid evidence for decision makers to implement policy.
Keywords: Mediator; Mediation analysis; Exposure; Outcome; Confounder; Interaction
Abbreviations: CHD: Congenital Heart Disease; ART: Assisted Reproductive Technology; CDE: Controlled Direct Effect; MI: Mediated Interaction; RI: Reference Interaction; PIE: Pure Indirect Effect
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Gary Kong, Qun Miao. New Direction: Mediation Analysis in Perinatal Epidemiological Studies. Annal Biostat & Biomed Appli. 3(5): 2020. ABBA.MS.ID.000573.
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This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.