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Article ; Online: A reply to the letter on '

Lee, Woojoo

Journal of minimally invasive surgery

2023 Volume 26, Issue 4, Page(s) 224–225

Language	English
Publishing date	2023-12-15
Publishing country	Korea (South)
Document type	Letter
ISSN	2234-5248
ISSN (online)	2234-5248
DOI	10.7602/jmis.2023.26.4.224
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Article ; Online: Directed acyclic graphs for clinical research: a tutorial.

Byeon, Sangmin / Lee, Woojoo

Journal of minimally invasive surgery

2023 Volume 26, Issue 3, Page(s) 97–107

Abstract: Directed acyclic graphs (DAGs) are useful tools for visualizing the hypothesized causal structures in an intuitive way and selecting relevant confounders in causal inference. However, in spite of their increasing use in clinical and surgical research, ... ...

Abstract	Directed acyclic graphs (DAGs) are useful tools for visualizing the hypothesized causal structures in an intuitive way and selecting relevant confounders in causal inference. However, in spite of their increasing use in clinical and surgical research, the causal graphs might also be misused by a lack of understanding of the central principles. In this article, we aim to introduce the basic terminology and fundamental rules of DAGs, and DAGitty, a user-friendly program that easily displays DAGs. Specifically, we describe how to determine variables that should or should not be adjusted based on the backdoor criterion with examples. In addition, the occurrence of the various types of biases is discussed with caveats, including the problem caused by the traditional approach using
Language	English
Publishing date	2023-09-15
Publishing country	Korea (South)
Document type	Journal Article ; Review
ISSN	2234-5248
ISSN (online)	2234-5248
DOI	10.7602/jmis.2023.26.3.97
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Article ; Online: An Introduction to Causal Mediation Analysis With a Comparison of 2 R Packages.

Byeon, Sangmin / Lee, Woojoo

Journal of preventive medicine and public health = Yebang Uihakhoe chi

2023 Volume 56, Issue 4, Page(s) 303–311

Abstract: Traditional mediation analysis, which relies on linear regression models, has faced criticism due to its limited suitability for cases involving different types of variables and complex covariates, such as interactions. This can result in unclear ... ...

Abstract	Traditional mediation analysis, which relies on linear regression models, has faced criticism due to its limited suitability for cases involving different types of variables and complex covariates, such as interactions. This can result in unclear definitions of direct and indirect effects. As an alternative, causal mediation analysis using the counterfactual framework has been introduced to provide clearer definitions of direct and indirect effects while allowing for more flexible modeling methods. However, the conceptual understanding of this approach based on the counterfactual framework remains challenging for applied researchers. To address this issue, the present article was written to highlight and illustrate the definitions of causal estimands, including controlled direct effect, natural direct effect, and natural indirect effect, based on the key concept of nested counterfactuals. Furthermore, we recommend using 2 R packages, 'medflex' and 'mediation', to perform causal mediation analysis and provide public health examples. The article also offers caveats and guidelines for accurate interpretation of the results.
MeSH term(s)	Humans ; Models, Statistical ; Mediation Analysis ; Causality ; Linear Models
Language	English
Publishing date	2023-07-31
Publishing country	Korea (South)
Document type	Journal Article
ZDB-ID	2620879-9
ISSN	2233-4521 ; 2233-4521
ISSN (online)	2233-4521
ISSN	2233-4521
DOI	10.3961/jpmph.23.189
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Article ; Online: Prediction of low-density lipoprotein cholesterol levels using machine learning methods.

Kim, Yoori / Lee, Won Kyung / Lee, Woojoo

Laboratory medicine

2024

Abstract: Objective: Low-density lipoprotein cholesterol (LDL-C) has been commonly calculated by equations, but their performance has not been entirely satisfactory. This study aimed to develop a more accurate LDL-C prediction model using machine learning methods. ...

Abstract	Objective: Low-density lipoprotein cholesterol (LDL-C) has been commonly calculated by equations, but their performance has not been entirely satisfactory. This study aimed to develop a more accurate LDL-C prediction model using machine learning methods. Methods: The study involved predicting directly measured LDL-C, using individual characteristics, lipid profiles, and other laboratory results as predictors. The models applied to predict LDL-C values were multiple regression, penalized regression, random forest, and XGBoost. Additionally, a novel 2-step prediction model was developed and introduced. The machine learning methods were evaluated against the Friedewald, Martin, and Sampson equations. Results: The Friedewald, Martin, and Sampson equations had root mean squared error (RMSE) values of 12.112, 8.084, and 8.492, respectively, whereas the 2-step prediction model showed the highest accuracy, with an RMSE of 7.015. The LDL-C levels were also classified as a categorical variable according to the diagnostic criteria of the dyslipidemia treatment guideline, and concordance rates were calculated between the predictive values obtained from each method and the directly measured ones. The 2-step prediction model had the highest concordance rate (85.1%). Conclusion: The machine learning method can calculate LDL-C more accurately than existing equations. The proposed 2-step prediction model, in particular, outperformed the other machine learning methods.
Language	English
Publishing date	2024-01-13
Publishing country	England
Document type	Journal Article
ZDB-ID	391758-7
ISSN	1943-7730 ; 0007-5027
ISSN (online)	1943-7730
ISSN	0007-5027
DOI	10.1093/labmed/lmad114
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Article ; Online: Application of Standardization for Causal Inference in Observational Studies: A Step-by-step Tutorial for Analysis Using R Software.

Lee, Sangwon / Lee, Woojoo

Journal of preventive medicine and public health = Yebang Uihakhoe chi

2022 Volume 55, Issue 2, Page(s) 116–124

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.
MeSH term(s)	Causality ; Humans ; Observational Studies as Topic ; Odds Ratio ; Probability ; Reference Standards ; Software
Language	English
Publishing date	2022-02-11
Publishing country	Korea (South)
Document type	Journal Article
ZDB-ID	2620879-9
ISSN	2233-4521 ; 2233-4521
ISSN (online)	2233-4521
ISSN	2233-4521
DOI	10.3961/jpmph.21.569
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Article: Characteristics of fatal occupational injuries in migrant workers in South Korea: A machine learning study.

Lee, Ju-Yeun / Lee, Woojoo / Cho, Sung-Il

Heliyon

2023 Volume 9, Issue 9, Page(s) e20138

Abstract: Objective: Analysis of occupational injuries is essential for developing preventive strategies. However, few studies have evaluated severe occupational injuries in migrant workers from the perspective of gender. Therefore, using a new analytical method, ...

Abstract	Objective: Analysis of occupational injuries is essential for developing preventive strategies. However, few studies have evaluated severe occupational injuries in migrant workers from the perspective of gender. Therefore, using a new analytical method, this study was performed to identify gender-specific characteristics associated with fatal occupational injuries among migrant workers; the interactions between these factors, were also analyzed. In addition, we compared the utility of explainable artificial intelligence (XAI) using SHapley Additive exPlanations (SHAP) with logistic regression (LR) and discuss caveats regarding its use. Materials and methods: We analyzed national statistics for occupational injuries among migrant workers ( Results: We found clear gender differences in fatal occupational injuries among migrant workers, with males in the same occupation having a higher risk of death than females. These gender differences suggest the need for gender-specific occupational injury prevention interventions for migrant workers to reduce the mortality rate. Occupation was a significant predictor of death among female migrant workers only, with care jobs having the highest fatality risk. The occupational fatality risk of female workers would not have been identified without the performance of detailed job-specific analyses stratified by gender. The major advantages of SHAP identified in the present study were the automatic identification and analysis of interactions, ability to determine the relative contributions of each feature, and high overall performance. The major caveat when using SHAP is that causality cannot be established. Conclusion: Detailed job-specific analyses stratified by gender, and interventions considering the gender of migrant workers, are necessary to reduce occupational fatality rates. The XAI approach should be considered as a complementary analytical method for epidemiological studies, as it overcomes the limitations of traditional statistical analyses.
Language	English
Publishing date	2023-09-14
Publishing country	England
Document type	Journal Article
ZDB-ID	2835763-2
ISSN	2405-8440
ISSN	2405-8440
DOI	10.1016/j.heliyon.2023.e20138
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Article: Revisiting the analysis pipeline for overdispersed Poisson and binomial data.

Lee, Woojoo / Kim, Jeonghwan / Lee, Donghwan

Journal of applied statistics

2022 Volume 50, Issue 7, Page(s) 1455–1476

Abstract: Overdispersion is a common feature in categorical data analysis and several methods have been developed for detecting and handling it in generalized linear models. The first aim of this study is to clarify the relationships among various score statistics ...

Abstract	Overdispersion is a common feature in categorical data analysis and several methods have been developed for detecting and handling it in generalized linear models. The first aim of this study is to clarify the relationships among various score statistics for testing overdispersion and to compare their performances. In addition, we investigate a principled way to correct finite sample bias in the score statistic caused by estimating regression parameters with restricted likelihood. The second aim is to reconsider the current practice for handling overdispersed categorical data. Although the conventional models are based on substantially different mechanisms for generating overdispersion, model selection in practice has not been well studied. We perform an intensive numerical study for determining which method is more robust to various overdispersion mechanisms. In addition, we provide some graphical tools for identifying the better model. The last aim is to reconsider the key assumption for deriving the score statistics. We study the meaning of testing overdispersion when this assumption is violated, and we analytically show the conditions for which it is not appropriate to employ the current statistical practices for analyzing overdispersed data.
Language	English
Publishing date	2022-01-20
Publishing country	England
Document type	Journal Article ; Review
ZDB-ID	1476802-1
ISSN	1360-0532 ; 0266-4763
ISSN (online)	1360-0532
ISSN	0266-4763
DOI	10.1080/02664763.2022.2026897
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Article ; Online: Overall assessment for selected markers from high-throughput data.

Lee, Woojoo / Lee, Donghwan / Pawitan, Yudi

Statistics in medicine

2022

Abstract: Reproducibility, a hallmark of science, is typically assessed in validation studies. We focus on high-throughput studies where a large number of biomarkers is measured in a training study, but only a subset of the most significant findings is selected ... ...

Abstract	Reproducibility, a hallmark of science, is typically assessed in validation studies. We focus on high-throughput studies where a large number of biomarkers is measured in a training study, but only a subset of the most significant findings is selected and re-tested in a validation study. Our aim is to get the statistical measures of overall assessment for the selected markers, by integrating the information in both the training and validation studies. Naive statistical measures, such as the combined
Language	English
Publishing date	2022-10-21
Publishing country	England
Document type	Journal Article
ZDB-ID	843037-8
ISSN	1097-0258 ; 0277-6715
ISSN (online)	1097-0258
ISSN	0277-6715
DOI	10.1002/sim.9596
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Article ; Online: On the finite sample distribution of the likelihood ratio statistic for testing heterogeneity in meta-analysis.

Kuk, Sunghee / Lee, Woojoo

Biometrical journal. Biometrische Zeitschrift

2020 Volume 62, Issue 8, Page(s) 1986–1996

Abstract: In meta-analysis, hypothesis testing is one of the commonly used approaches for assessing whether heterogeneity exists in effects between studies. The literature concluded that the Q-statistic is clearly the best choice and criticized the performance of ... ...

Abstract	In meta-analysis, hypothesis testing is one of the commonly used approaches for assessing whether heterogeneity exists in effects between studies. The literature concluded that the Q-statistic is clearly the best choice and criticized the performance of the likelihood ratio test in terms of the type I error control and power. However, all the criticism for the likelihood ratio test is based on the use of a mixture of two chi-square distributions with 0 and 1 degrees of freedom, which is justified only asymptotically. In this study, we develop a novel method to derive the finite sample distribution of the likelihood ratio test and restricted likelihood ratio test statistics for testing the zero variance component in the random effects model for meta-analysis. We also extend this result to the heterogeneity test when metaregression is applied. A numerical study shows that the proposed statistics have superior performance to the Q-statistic, especially when the number of studies collected for meta-analysis is small to moderate.
Language	English
Publishing date	2020-08-05
Publishing country	Germany
Document type	Journal Article
ZDB-ID	131640-0
ISSN	1521-4036 ; 0323-3847 ; 0006-3452
ISSN (online)	1521-4036
ISSN	0323-3847 ; 0006-3452
DOI	10.1002/bimj.201900400
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Article ; Online: Associations of water, sanitation, and hygiene with typhoid fever in case-control studies: a systematic review and meta-analysis.

Kim, Chaelin / Goucher, Gerard R / Tadesse, Birkneh Tilahun / Lee, Woojoo / Abbas, Kaja / Kim, Jong-Hoon

BMC infectious diseases

2023 Volume 23, Issue 1, Page(s) 562

Abstract: Background: Water, sanitation, and hygiene (WASH) play a pivotal role in controlling typhoid fever, as it is primarily transmitted through oral-fecal pathways. Given our constrained resources, staying current with the most recent research is crucial. ... ...

Abstract	Background: Water, sanitation, and hygiene (WASH) play a pivotal role in controlling typhoid fever, as it is primarily transmitted through oral-fecal pathways. Given our constrained resources, staying current with the most recent research is crucial. This ensures we remain informed about practical insights regarding effective typhoid fever control strategies across various WASH components. We conducted a systematic review and meta-analysis of case-control studies to estimate the associations of water, sanitation, and hygiene exposures with typhoid fever. Methods: We updated the previous review conducted by Brockett et al. We included new findings published between June 2018 and October 2022 in Web of Science, Embase, and PubMed. We used the Risk of Bias in Non-Randomized Studies of Interventions (ROBINS-I) tool for risk of bias (ROB) assessment. We classified WASH exposures according to the classification provided by the WHO/UNICEF Joint Monitoring Programme for Water Supply, Sanitation, and Hygiene (JMP) update in 2015. We conducted the meta-analyses by only including studies that did not have a critical ROB in both Bayesian and frequentist random-effects models. Results: We identified 8 new studies and analyzed 27 studies in total. Our analyses showed that while the general insights on the protective (or harmful) impact of improved (or unimproved) WASH remain the same, the pooled estimates of OR differed. Pooled estimates of limited hygiene (OR = 2.26, 95% CrI: 1.38 to 3.64), untreated water (OR = 1.96, 95% CrI: 1.28 to 3.27) and surface water (OR = 2.14, 95% CrI: 1.03 to 4.06) showed 3% increase, 18% decrease, and 16% increase, respectively, from the existing estimates. On the other hand, improved WASH reduced the odds of typhoid fever with pooled estimates for improved water source (OR = 0.54, 95% CrI: 0.31 to 1.08), basic hygiene (OR = 0.6, 95% CrI: 0.38 to 0.97) and treated water (OR = 0.54, 95% CrI: 0.36 to 0.8) showing 26% decrease, 15% increase, and 8% decrease, respectively, from the existing estimates. Conclusions: The updated pooled estimates of ORs for the association of WASH with typhoid fever showed clear changes from the existing estimates. Our study affirms that relatively low-cost WASH strategies such as basic hygiene or water treatment can be an effective tool to provide protection against typhoid fever in addition to other resource-intensive ways to improve WASH. Trial registration: PROSPERO 2021 CRD42021271881.
MeSH term(s)	Humans ; Sanitation ; Bayes Theorem ; Typhoid Fever/epidemiology ; Typhoid Fever/prevention & control ; Case-Control Studies ; Hygiene
Language	English
Publishing date	2023-08-29
Publishing country	England
Document type	Meta-Analysis ; Systematic Review ; Journal Article
ZDB-ID	2041550-3
ISSN	1471-2334 ; 1471-2334
ISSN (online)	1471-2334
ISSN	1471-2334
DOI	10.1186/s12879-023-08452-0
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