Methodological Tutorial Series for Epidemiological Studies: Confounder Selection and Sensitivity Analyses to Unmeasured Confounding From Epidemiological and Statistical Perspectives
In observational studies, identifying and adjusting for a sufficient set of confounders is crucial for accurately estimating the causal effect of the exposure on the outcome. Even in studies with large sample sizes, which typically benefit from small variances in estimates, there is a risk of produc...
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Japan Epidemiological Association
2025-01-01
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| Series: | Journal of Epidemiology |
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| Online Access: | https://www.jstage.jst.go.jp/article/jea/35/1/35_JE20240082/_pdf |
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| author | Kosuke Inoue Kentaro Sakamaki Sho Komukai Yuri Ito Atsushi Goto Tomohiro Shinozaki |
| author_facet | Kosuke Inoue Kentaro Sakamaki Sho Komukai Yuri Ito Atsushi Goto Tomohiro Shinozaki |
| author_sort | Kosuke Inoue |
| collection | DOAJ |
| description | In observational studies, identifying and adjusting for a sufficient set of confounders is crucial for accurately estimating the causal effect of the exposure on the outcome. Even in studies with large sample sizes, which typically benefit from small variances in estimates, there is a risk of producing estimates that are precisely inaccurate if the study suffers from systematic errors or biases, including confounding bias. To date, several approaches have been developed for selecting confounders. In this article, we first summarize the epidemiological and statistical approaches to identifying a sufficient set of confounders. Particularly, we introduce the modified disjunctive cause criterion as one of the most useful approaches, which involves controlling for any pre-exposure covariate that affects the exposure, outcome, or both. It then excludes instrumental variables but includes proxies for the shared common cause of exposure and outcome. Statistical confounder selection is also useful when dealing with a large number of covariates, even in studies with small sample sizes. After introducing several approaches, we discuss some pitfalls and considerations in confounder selection, such as the adjustment for instrumental variables, intermediate variables, and baseline outcome variables. Lastly, as it is often difficult to comprehensively measure key confounders, we introduce two statistics, E-value and robustness value, for assessing sensitivity to unmeasured confounders. Illustrated examples are provided using the National Health and Nutritional Examination Survey Epidemiologic Follow-up Study. Integrating these principles and approaches will enhance our understanding of confounder selection and facilitate better reporting and interpretation of future epidemiological studies. |
| format | Article |
| id | doaj-art-216d6580f47f4fb6850ab3b9e4b88285 |
| institution | Kabale University |
| issn | 0917-5040 1349-9092 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Japan Epidemiological Association |
| record_format | Article |
| series | Journal of Epidemiology |
| spelling | doaj-art-216d6580f47f4fb6850ab3b9e4b882852025-01-06T03:54:45ZengJapan Epidemiological AssociationJournal of Epidemiology0917-50401349-90922025-01-0135131010.2188/jea.JE20240082Methodological Tutorial Series for Epidemiological Studies: Confounder Selection and Sensitivity Analyses to Unmeasured Confounding From Epidemiological and Statistical PerspectivesKosuke Inoue0Kentaro Sakamaki1Sho Komukai2Yuri Ito3Atsushi Goto4Tomohiro Shinozaki5Department of Social Epidemiology, Graduate School of Medicine, Kyoto University, Kyoto, JapanCenter for Data Science, Yokohama City University, Yokohama, JapanDivision of Biomedical Statistics, Department of Integrated Medicine, Graduate School of Medicine, Osaka University, Osaka, JapanDepartment of Medical Statistics, Research & Development Center, Osaka Medical and Pharmaceutical University, Osaka, JapanDepartment of Public Health, School of Medicine, Yokohama City University, Yokohama, JapanDepartment of Information and Computer Technology, Faculty of Engineering, Tokyo University of Science, Tokyo, JapanIn observational studies, identifying and adjusting for a sufficient set of confounders is crucial for accurately estimating the causal effect of the exposure on the outcome. Even in studies with large sample sizes, which typically benefit from small variances in estimates, there is a risk of producing estimates that are precisely inaccurate if the study suffers from systematic errors or biases, including confounding bias. To date, several approaches have been developed for selecting confounders. In this article, we first summarize the epidemiological and statistical approaches to identifying a sufficient set of confounders. Particularly, we introduce the modified disjunctive cause criterion as one of the most useful approaches, which involves controlling for any pre-exposure covariate that affects the exposure, outcome, or both. It then excludes instrumental variables but includes proxies for the shared common cause of exposure and outcome. Statistical confounder selection is also useful when dealing with a large number of covariates, even in studies with small sample sizes. After introducing several approaches, we discuss some pitfalls and considerations in confounder selection, such as the adjustment for instrumental variables, intermediate variables, and baseline outcome variables. Lastly, as it is often difficult to comprehensively measure key confounders, we introduce two statistics, E-value and robustness value, for assessing sensitivity to unmeasured confounders. Illustrated examples are provided using the National Health and Nutritional Examination Survey Epidemiologic Follow-up Study. Integrating these principles and approaches will enhance our understanding of confounder selection and facilitate better reporting and interpretation of future epidemiological studies.https://www.jstage.jst.go.jp/article/jea/35/1/35_JE20240082/_pdfconfounderconfoundingdagsensitivity analysis |
| spellingShingle | Kosuke Inoue Kentaro Sakamaki Sho Komukai Yuri Ito Atsushi Goto Tomohiro Shinozaki Methodological Tutorial Series for Epidemiological Studies: Confounder Selection and Sensitivity Analyses to Unmeasured Confounding From Epidemiological and Statistical Perspectives Journal of Epidemiology confounder confounding dag sensitivity analysis |
| title | Methodological Tutorial Series for Epidemiological Studies: Confounder Selection and Sensitivity Analyses to Unmeasured Confounding From Epidemiological and Statistical Perspectives |
| title_full | Methodological Tutorial Series for Epidemiological Studies: Confounder Selection and Sensitivity Analyses to Unmeasured Confounding From Epidemiological and Statistical Perspectives |
| title_fullStr | Methodological Tutorial Series for Epidemiological Studies: Confounder Selection and Sensitivity Analyses to Unmeasured Confounding From Epidemiological and Statistical Perspectives |
| title_full_unstemmed | Methodological Tutorial Series for Epidemiological Studies: Confounder Selection and Sensitivity Analyses to Unmeasured Confounding From Epidemiological and Statistical Perspectives |
| title_short | Methodological Tutorial Series for Epidemiological Studies: Confounder Selection and Sensitivity Analyses to Unmeasured Confounding From Epidemiological and Statistical Perspectives |
| title_sort | methodological tutorial series for epidemiological studies confounder selection and sensitivity analyses to unmeasured confounding from epidemiological and statistical perspectives |
| topic | confounder confounding dag sensitivity analysis |
| url | https://www.jstage.jst.go.jp/article/jea/35/1/35_JE20240082/_pdf |
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