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KMID : 1044620220550020116
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Journal of Preventive Medicine and Public Health
2022 Volume.55 No. 2 p.116 ~ p.124
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Application of Standardization for Causal Inference in Observational Studies: A Step-by-step Tutorial for Analysis Using R Software
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Lee Sang-Won
Lee Woo-Joo
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Abstract
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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.
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KEYWORD
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Standardization, Causality, Observational study, Confounding factors, Epidemiology
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