Journal of Preventive Medicine and Public Health

Special Article

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

5,167 View
275 Download
3 Web of Science
3 Crossref

Abstract Summary PDF 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.

Summary

Korean 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

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.2024; 18(5): 738. CrossRef
Cross-Sectoral Comparisons of Process Quality Indicators of Health Care Across Residential Regions Using Restricted Mean Survival Time
Hana Šinkovec, Walter Gall, Georg Heinze
Medical Care.2024; 62(11): 748. CrossRef
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

English Abstract

Strengthening Causal Inference in Studies using Non-experimental Data: An Application of Propensity Score and Instrumental Variable Methods.: Myoung Hee Kim, Young Kyung Do; J Prev Med Public Health. 2007;40(6):495-504.; DOI: https://doi.org/10.3961/jpmph.2007.40.6.495

5,310 View
90 Download
8 Crossref

Abstract PDF

OBJECTIVES
This study attempts to show how studies using non-experimental data can strengthen causal inferences by applying propensity score and instrumental variable methods based on the counterfactual framework. For illustrative purposes, we examine the effect of having private health insurance on the probability of experiencing at least one hospital admission in the previous year. METHODS: Using data from the 4th wave of the Korea Labor and Income Panel Study, we compared the results obtained using propensity score and instrumental variable methods with those from conventional logistic and linear regression models, respectively. RESULTS: While conventional multiple regression analyses fail to identify the effect, the results estimated using propensity score and instrumental variable methods suggest that having private health insurance has positive and statistically significant effects on hospital admission. CONCLUSIONS: This study demonstrates that propensity score and instrumental variable methods provide potentially useful alternatives to conventional regression approaches in making causal inferences using non-experimental data.

Summary

Citations

Citations to this article as recorded by

Association between private health insurance and medical use by linking subjective health and chronic diseases
Jeong Min Yang, Su bin Lee, Ye ji Kim, Douk young Chon, Jong Youn Moon, Jae Hyun Kim
Medicine.2022; 101(32): e29865. CrossRef
Gender-related difference in the relationship between smoking status and periodontal diseases: the propensity score matching approach
Eun-Sil Choi, Hae-Young Kim
Journal of Korean Academy of Oral Health.2017; 41(2): 122. CrossRef
The Effect of Obesity on Medical Costs and Health Service Uses
Da-Yang Kim, Jin-Mi Kwak, So-Young Choi, Kwang-Soo Lee
The Korean Journal of Health Service Management.2017; 11(3): 65. CrossRef
Effect of private health insurance on health care utilization in a universal public insurance system: A case of South Korea
Boyoung Jeon, Soonman Kwon
Health Policy.2013; 113(1-2): 69. CrossRef
Health Disparities among Wage Workers Driven by Employment Instability in the Republic of Korea
Minsoo Jung
International Journal of Health Services.2013; 43(3): 483. CrossRef
Survey of Editors and Reviewers of High-Impact Psychology Journals: Statistical and Research Design Problems in Submitted Manuscripts
Alex Harris, Rachelle Reeder, Jenny Hyun
The Journal of Psychology.2011; 145(3): 195. CrossRef
Limitations of the SEER Database for Demonstrating Causal Relationships Between Treatments and Outcomes in Pediatric Intestinal Tumors
Alysandra Lal, Dave R. Lal
Journal of Surgical Research.2010; 161(2): 237. CrossRef
Common statistical and research design problems in manuscripts submitted to high-impact psychiatry journals: What editors and reviewers want authors to know
Alex H.S. Harris, Rachelle Reeder, Jenny K. Hyun
Journal of Psychiatric Research.2009; 43(15): 1231. CrossRef

First
Prev
Page of 1
Next
Last

Search