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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. -
Citations
Citations to this article as recorded by
- Homologous and Heterologous Prime-Boost Vaccination: Impact on Clinical Severity of SARS-CoV-2 Omicron Infection among Hospitalized COVID-19 Patients in Belgium
Marjan Meurisse, Lucy Catteau, Joris A. F. van Loenhout, Toon Braeye, Laurane De Mot, Ben Serrien, Koen Blot, Emilie Cauët, Herman Van Oyen, Lize Cuypers, Annie Robert, Nina Van Goethem
Vaccines.2023; 11(2): 378. CrossRef - Improved Clinical Outcomes With Early Anti-Tumour Necrosis Factor Alpha Therapy in Children With Newly Diagnosed Crohn’s Disease: Real-world Data from the International Prospective PIBD-SETQuality Inception Cohort Study
Renz C W Klomberg, Hella C van der Wal, Martine A Aardoom, Polychronis Kemos, Dimitris Rizopoulos, Frank M Ruemmele, Mohammed Charrout, Hankje C Escher, Nicholas M Croft, Lissy de Ridder, Ivan D Milovanovich, James J Ashton, Paul Henderson, Oren Ledder, T
Journal of Crohn's and Colitis.2023;[Epub] CrossRef
- Homologous and Heterologous Prime-Boost Vaccination: Impact on Clinical Severity of SARS-CoV-2 Omicron Infection among Hospitalized COVID-19 Patients in Belgium
Original Article
- Assessment of Applicability of Standardized Rates for Health State Comparison Among Areas: 2008 Community Health Survey.
- Geun Yong Kwon, Do Sang Lim, Eun Ja Park, Ji Sun Jung, Ki Won Kang, Yun A Kim, Ho Kim, Sung Il Cho
- J Prev Med Public Health. 2010;43(2):174-184.
- DOI: https://doi.org/10.3961/jpmph.2010.43.2.174
- 5,269 View
- 47 Download
- 6 Crossref
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Abstract
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- OBJECTIVES
This study shows the issues that should be considered when applying standardized rates using Community Health Survey(CHS) data. METHODS: We analyzed 2008 CHS data. In order to obtain the reliability of standardized rates, we calculated z-score and rank correlation coefficients between direct standardized rate and indirect standardized rate for 31 major indices. Especially, we assessed the change of correlations according to population composition (age and sex), and characteristics of the index. We used Mantel-Haenszel chi-square to quantify the difference of population composition. RESULTS: Among 31 major indices, 29 indices' z-score and rank correlation coefficients were over 0.9. However, regions with larger differences in population composition showed lower reliability. Low reliability was also observed for the indices specific to subgroups with small denominator such as 'permanent lesion from stroke', and the index with large regional variations in age-related differences such as 'obtaining health examinations'. CONCLUSIONS: Standardized rates may have low reliability, if comparison is made between areas with extremely large differences in population composition, or for indicies with large regional variations in age-related differences. Therefore, the special features of standardized rates should be considered when health state are compared among areas. -
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Citations
Citations to this article as recorded by- Ambient air quality and subjective stress level using Community Health Survey data in Korea
Myung-Jae Hwang, Hae-Kwan Cheong, Jong-Hun Kim, Youn Seo Koo, Hui-Young Yun
Epidemiology and Health.2018; 40: e2018028. CrossRef - Illustration of Calculating Standardized Rates Utilizing Logistic Regression Models: The National Health Insurance Service-National Health Screening Cohort (NHIS-HEALS)
Sang-Hoon Cho, Gunseog Kang, Hyeon Chang Kim
Journal of Health Informatics and Statistics.2017; 42(1): 70. CrossRef - Korea Community Health Survey Data Profiles
Yang Wha Kang, Yun Sil Ko, Yoo Jin Kim, Kyoung Mi Sung, Hyo Jin Kim, Hyung Yun Choi, Changhyun Sung, Eunkyeong Jeong
Osong Public Health and Research Perspectives.2015; 6(3): 211. CrossRef - Health behavior affecting on the regional variation of standardized mortality
Jin A Han, Soo Jeong Kim, Se Rom Kim, Ki Hong Chun, Yun Hwan Lee, Soon Young Lee
Korean Journal of Health Education and Promotion.2015; 32(3): 23. CrossRef - Convergence-based analysis on geographical variations of the smoking rates
Ji-Hye Lim, Sung-Hong Kang
Journal of Digital Convergence.2015; 13(8): 375. CrossRef - Overview of Korean Community Health Survey
Young Taek Kim, Bo Youl Choi, Kay O Lee, Ho Kim, Jin Ho Chun, Su Young Kim, Duk-Hyoung Lee, Yun A Ghim, Do Sang Lim, Yang Wha Kang, Tae Young Lee, Jeong Sook Kim, Hyun Jo, Yoojin Kim, Yun Sil Ko, Soon Ryu Seo, No-Rye Park, Jong-Koo Lee
Journal of the Korean Medical Association.2012; 55(1): 74. CrossRef
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