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- Application of Standardization for Causal Inference in Observational Studies: A Step-by-step Tutorial for Analysis Using R Software
- Sangwon Lee, Woojoo Lee
- J Prev Med Public Health. 2022;55(2):116-124. Published online February 11, 2022
- DOI: https://doi.org/10.3961/jpmph.21.569
- 3,629 View
- 235 Download
- 2 Web of Science
- 2 Crossref
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Abstract
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Supplementary Material - 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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SummaryKorean summary
본 논문에서는 standardization 방법을 이용하여 risk difference, relative risk, risk ratio와 같은 인과성 효과를 R software을 이용하여 도출하는 튜토리얼을 제공합니다. 간암환자의 치료를 예시로, 합성 데이터를 이용한 치료제의 사망에 대한 인과적 효과를 탐색하는 튜토리얼을 제공합니다. 추가적으로, 인과성 관련 기본 이론을 집약적으로 설명하였고, standardization을 이용한 subgroup analysis 수행 방법이 제공됩니다. -
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