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Special Article
Introduction to Mediation Analysis and Examples of Its Application to Real-world Data
Sun Jae Jung
J Prev Med Public Health. 2021;54(3):166-172.   Published online May 7, 2021
DOI: https://doi.org/10.3961/jpmph.21.069
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  • 402 Download
  • 13 Web of Science
  • 16 Crossref
AbstractAbstract AbstractSummary PDF
Traditional epidemiological assessments, which mainly focused on evaluating the statistical association between two major components-the exposure and outcome-have recently evolved to ascertain the in-between process, which can explain the underlying causal pathway. Mediation analysis has emerged as a compelling method to disentangle the complex nature of these pathways. The statistical method of mediation analysis has evolved from simple regression analysis to causal mediation analysis, and each amendment refined the underlying mathematical theory and required assumptions. This short guide will introduce the basic statistical framework and assumptions of both traditional and modern mediation analyses, providing examples conducted with real-world data.
Summary
Korean summary
본 리뷰에서는 전통적으로 쓰여왔던 회귀분석 기반 매개요인분석방법과 인과적 매개요인분석방법에 대하여 간략히 소개하며, 또한 예시를 들어 설명하고 있다. 매개요인 분석 전에는 필요한 가정들이 맞는 지를 확인하는 것이 중요하다. 또한 분석결과를 올바르게 해석하는 것 또한 요구된다. 이러한 방법은 그동안의 노출-결과만을 다루는 단순 역학연구을 뛰어넘어, 인구집단연구에서 매커니즘을 밝히는 도구로서 유용하게 쓰일 것이다.

Citations

Citations to this article as recorded by  
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    Environmental Research.2024; 240: 117418.     CrossRef
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    Qu Tian, Philip R. Lee, Qi Yang, Anne Z. Moore, Bennett A. Landman, Susan M. Resnick, Luigi Ferrucci
    Journal of Cachexia, Sarcopenia and Muscle.2024; 15(1): 138.     CrossRef
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    Nicholas Grubic, Braeden Hill, Katherine S. Allan, Katerina Maximova, Hailey R. Banack, Marina del Rios, Amer M. Johri
    Canadian Journal of Cardiology.2024;[Epub]     CrossRef
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    Stephanie Guzman, Robert D. Melara
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    Xiaoyu Tian, Xiaobing Shan, Li Ma, Chenyang Zhang, Mei Wang, Jie Zheng, Ruoyi Lei, Li He, Jun Yan, Xun Li, Yanjun Bai, Keqin Hu, Sheng Li, Jingping Niu, Bin Luo
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    Chinodebem Ogbutor, Stephanie M. Mishaw, Zuber D. Mulla
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English Abstract
Estimation of Smoking Prevalence among Adolescents in a Community by Design-based Analysis.
Soon Woo Park, Jong Yeon Kim, Sang Won Lee, Jung Han Park, Yeon Oh Yun, Won Kee Lee
J Prev Med Public Health. 2006;39(4):317-324.
  • 2,165 View
  • 27 Download
AbstractAbstract PDF
OBJECTIVES
This study was conducted to estimate the unbiased smoking prevalence and its standard errors among adolescents in a large city in Korea, by designbased analysis. METHODS: All the students in Daegu city were stratified by grade, gender and region, and then schools as primary sampling units (PSU) were selected by probability proportional to size (PPS) sampling. One or two classes were sampled randomly from each grade, from 5th grade in elementary schools to the 3rd grade in high schools. The students anonymously completed a standardized selfadministered questionnaire from October to December 2004. The total number of respondents was 8,480 in the final analysis, excluding the third graders in the general high schools because of incomplete sampling. The sampling weight was calculated for each student after poststratification adjustment, with adjustment being made for the missing cases. The data were analyzed with Stata 8.0 with consideration of PSU, weighting and the strata variables. RESULTS: The smoking prevalence (%) and standard errors for male students from the fifth grade in elementary schools to the second grade in high schools were 0.93[0.47, 1.83[ 0.74, 3.16[1.00, 5.12[1.02, 10.86[1.13, 15.63[2.44 and 17.96[2.67, and those for the female students were 0.28[ 0.28, 1.17[0.73, 3.13[0.60, 1.45[0.58, 3.94[0.92, 8.75 [1.86 and 10.04[1.70, sequentially. CONCLUSIONS: The smoking prevalence from this study was much higher than those from the other conventional studies conducted in Korea. The point estimates and standard errors from the design-based analysis were different from those of the model-based analysis. These findings suggest the importance of design-based analysis to estimate unbiased prevalence and standard errors in complex survey data and this method is recommended to apply to future surveys for determining the smoking prevalence for specific population.
Summary

JPMPH : Journal of Preventive Medicine and Public Health