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http://dx.doi.org/10.3961/jpmph.21.569

Application of Standardization for Causal Inference in Observational Studies: A Step-by-step Tutorial for Analysis Using R Software  

Lee, Sangwon (Department of Public Health Science, Graduate School of Public Health, Seoul National University)
Lee, Woojoo (Department of Public Health Science, Graduate School of Public Health, Seoul National University)
Publication Information
Journal of Preventive Medicine and Public Health / v.55, no.2, 2022 , pp. 116-124 More about this Journal
Abstract
Epidemiological studies typically examine the causal effect of exposure on a health outcome. Standardization is one of the most straightforward methods for estimating causal estimands. However, compared to inverse probability weighting, there is a lack of user-centric explanations for implementing standardization to estimate causal estimands. This paper explains the standardization method using basic R functions only and how it is linked to the R package stdReg, which can be used to implement the same procedure. We provide a step-by-step tutorial for estimating causal risk differences, causal risk ratios, and causal odds ratios based on standardization. We also discuss how to carry out subgroup analysis in detail.
Keywords
Standardization; Causality; Observational study; Confounding factors; Epidemiology;
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