J Prev Med Public Health.
2022 Mar;55(2):116-124. 10.3961/jpmph.21.569.
Application of Standardization for Causal Inference in Observational Studies: A Step-by-step Tutorial for Analysis Using R Software
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- 1Department of Public Health Science, Graduate School of Public Health, Seoul National University, Seoul, Korea
- KMID: 2528303
- DOI: http://doi.org/10.3961/jpmph.21.569
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.
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