Article ; Online: Application of Standardization for Causal Inference in Observational Studies
Journal of Preventive Medicine and Public Health, Vol 55, Iss 2, Pp 116-
A Step-by-step Tutorial for Analysis Using R Software
2022 Volume 124
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 ... ...
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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Keywords | standardization ; causality ; observational study ; confounding factors ; epidemiology ; Medicine ; R ; Public aspects of medicine ; RA1-1270 |
Language | English |
Publishing date | 2022-03-01T00:00:00Z |
Publisher | Korean Society for Preventive Medicine |
Document type | Article ; Online |
Database | BASE - Bielefeld Academic Search Engine (life sciences selection) |
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