• Journal of Preventive Medicine and Public Health
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• v.55 no.2
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• pp.116-124
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• 2022

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.

• Communications for Statistical Applications and Methods
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• v.29 no.2
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• pp.177-191
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• 2022

This paper addresses the use of machine learning methods for causal estimation of treatment effects from observational data. Even though conducting randomized experimental trials is a gold standard to reveal potential causal relationships, observational study is another rich source for investigation of exposure effects, for example, in the research of comparative effectiveness and safety of treatments, where the causal effect can be identified if covariates contain all confounding variables. In this context, statistical regression models for the expected outcome and the probability of treatment are often imposed, which can be combined in a clever way to yield more efficient and robust causal estimators. Recently, targeted maximum likelihood estimation and causal random forest is proposed and extensively studied for the use of data-adaptive regression in estimation of causal inference parameters. Machine learning methods are a natural choice in these settings to improve the quality of the final estimate of the treatment effect. We explore how we can adapt the design and training of several machine learning algorithms for causal inference and study their finite-sample performance through simulation experiments under various scenarios. Application to the percutaneous coronary intervention (PCI) data shows that these adaptations can improve simple linear regression-based methods.

• Therapeutic Science for Rehabilitation
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• v.12 no.3
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• pp.61-77
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• 2023

Objective : This study analyzed the correlation between cognitive function and oral health-related quality of life (OHQoL). Methods : Demographic and clinical characteristics were extracted and utilized for subjects aged 45 years or older who participated in the 8th Korean Longitudinal Study on Aging in 2020. The dependent variable was the Geriatric Oral Health Assessment Index, and the independent variable was the level of cognitive function classified by the Mini-Mental State Examination scores. The analysis method used inverse probability of treatment weighting (IPTW). Then, the association between cognitive function and OHQoL was analyzed by multiple regression analysis. Results : Among the participants, 4,367 (71.40%) had normal cognition, 1,155 (18.89%) had moderate cognitive impairment, and 594 (9.71%) had severe cognitive impairment. As a result of analysis by applying IPTW, there was a negative correlation between the cognitive function group and OHQoL (normal vs. moderate: β = -2.534, p < .0001; normal vs. severe: β = -2.452, p < .0001). Conclusion : After propensity score matching, mild cognitive impairment showed a more negative association than severe cognitive impairment. Therefore, patients with cognitive impairment require oral health management education to improve OHQoL regardless of the level of cognitive impairment.

• Health Policy and Management
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• v.34 no.1
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• pp.68-77
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• 2024

Background: Depressive disorders can be categorized into daily depression and clinical depression. The experience of depressive disorder can increase health care utilization due to decreased treatment compliance and somatization. On the other hand, the clinical depression group may also experience social prejudice associated with the illness, which can limit their access to health care utilization. In terms of the significance of health care utilization as a factor in individual and social issues, this study aims to compare the health care utilization of the clinical depression group with that of the non-depressed group and the daily depression group. Methods: The analysis utilized the inverse probability of treatment weighting based on the generalized propensity score. Results: As a result of the analysis, clinical depression and daily depression were higher among women, low-income groups, individuals with low education levels, and so forth. The clinical depression group was also higher among individuals who were not economically active, did not have private health insurance, or had multiple chronic diseases. The number of outpatient department visits in the depression group was significantly higher than in the non-depressed group. In addition, the number of outpatient department visits for the clinical depression group was significantly higher than that for the daily depression group. Outpatient medical expenses were higher in the depression group than in the non-depressed group, and there was no significant difference between the clinical depression group and the daily depression group. Conclusion: Health care utilization was higher in the depression group than the non-depressed group, it was also higher in the clinical depression group than the daily depression group.

• Korean Journal of Clinical Pharmacy
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• v.29 no.2
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• pp.109-114
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• 2019

Background: Rheumatoid arthritis (RA) is a systemic inflammatory disease that manifests as joint damage or athletic disability via sustained inflammation of the synovial membrane. The risk of cardiovascular disease (CVD) is higher in RA patients. This study aimed at evaluating the association between CVD comorbidities and RA by comparing a pharmacotherapy group with a non-pharmacotherapy group. Methods: Patient sample data from the Health Insurance Review and Assessment Service (HIRA-NPS-2016) were used. Inverse probability of treatment weighting (IPTW) using the propensity score was used to minimize the differences in patient characteristics. Logistic regression analysis was used to evaluate the risk of CVD comorbidities. Results: The analyses included 1,207,213 patients, of which 33,122 (2.8%) had RA. The odds ratios (OR) of CVD comorbidities were increased in RA patients; ischemic heart disease (IHD: OR 1.75; 95% CI 1.73, 1.77), cerebral infarction (CERI: OR 1.28; 95% CI 1.26, 1.30), hypertension (HTN: OR 1.44; 95% CI 1.43, 1.45), diabetes mellitus (DM: OR 2.04; 95% CI 2.03, 2.06), and dyslipidemia (DL: OR 3.49; 95% CI 3.47, 3.51). The ORs of IHD, CERI, HTN, and DM in the traditional DMARD and biologic treatment groups were decreased, compared with those in the non-pharmacotherapy group. Conclusions: Thus, CVD risk was higher in RA patients, considering age, sex, and socioeconomic status. Appropriate pharmacotherapy could decrease the risk of CVD comorbidities in RA patients.

• The Korean Journal of Applied Statistics
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• v.35 no.1
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• pp.35-47
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• 2022

The receiver operating characteristic (ROC) curve was developed to quantify the classification ability of marker values (covariates) on the response variable and has been extended to survival data with diverse missing data structure. When survival data is understood as binary data (status of being alive or dead) at each time point, the ROC curve expressed at every time point results in time-dependent ROC curve and time-dependent area under curve (AUC). In particular, a follow-up study brings the change of cohort and incomplete data structures such as censoring and competing risk. In this paper, we review time-dependent ROC estimators under several contexts and perform simulation to check the performance of each estimators. We analyzed a dementia dataset to compare the prognostic power of markers.

• Korean Journal of Clinical Pharmacy
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• v.30 no.4
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• pp.264-269
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• 2020

Background: Inflammatory diseases can increase the prevalence of anemia. Recent studies confirmed that the prevalence of anemia is increased by atopic dermatitis (AD), a chronic inflammatory disease. Therefore, we aimed to elucidate the correlation between AD severity and prevalence of anemia. Methods: We used data of pediatric patients from the Health Insurance Review and Assessment Service (HIRA-PPS-2016). We included pediatric patients (<18 years) with AD diagnosis who were prescribed medications for AD. We applied a propensity score method with inverse probability of treatment weighting (IPTW) adjusting for differences in prevalence of confounders and performed IPTW logistic regression to evaluate associations between the anemia and severity of AD. Results: In total, 91,501 patients (mild AD: 47,054 patients; moderate-to-severe AD: 44,447 patients) <18 years who were prescribed drugs for AD were analyzed. Analysis of the probability of patients with mild AD and prevalence of anemia as a reference revealed an odds ratio (OR) of 1.159 (95% CI, 1.109-1.212; p<0.001) in moderate-to-severe AD patients, indicating a correlation between anemia prevalence and AD severity. Subgroup analysis according to gender, age group, and type of health insurance revealed there was an association between AD severity and anemia except in patients equal or older than 7 years. Conclusion: The prevalence of anemia increased with AD severity despite adjusting for confounding factors. Our results support the hypothesis that AD can cause anemia, and anemia prevalence could be increased in severe AD patients. Further studies are needed to establish a pathological basis.

• Journal of Preventive Medicine and Public Health
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• v.53 no.6
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• pp.476-486
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• 2020

Objectives: The objective of this study was to estimate the risk of lung cancer in relation to angiotensin II receptor blocker (ARB) use among patients with hypertension from the Korean National Health Insurance Service-National Health Screening Cohort. Methods: We conducted a retrospective cohort study of patients with hypertension who started to take antihypertensive medications and had a treatment period of at least 6 months. We calculated the weighted hazard ratios (HRs) and their 95% confidence intervals (CIs) of lung cancer associated with ARB use compared with calcium channel blocker (CCB) use using inverse probability treatment weighting. Results: Among a total of 60 469 subjects with a median follow-up time of 7.8 years, 476 cases of lung cancer were identified. ARB use had a protective effect on lung cancer compared with CCB use (HR, 0.75; 95% CI, 0.59 to 0.96). Consistent findings were found in analyses considering patients who changed or discontinued their medication (HR, 0.50; 95% CI, 0.32 to 0.77), as well as for women (HR, 0.56; 95% CI, 0.34 to 0.93), patients without chronic obstructive pulmonary disease (HR, 0.75; 95% CI, 0.56 to 1.00), never-smokers (HR, 0.64; 95% CI, 0.42 to 0.99), and non-drinkers (HR, 0.69; 95% CI, 0.49 to 0.97). In analyses with different comparison antihypertensive medications, the overall protective effects of ARBs on lung cancer risk remained consistent. Conclusions: The results of the present study suggest that ARBs could decrease the risk of lung cancer. More evidence is needed to establish the causal effect of ARBs on the incidence of lung cancer.

• The Korean Journal of Applied Statistics
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• v.27 no.7
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• pp.1077-1095
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• 2014

Unlike randomized trial, statistical strategies for inferring the unbiased causal relationship are required in the observational studies. Recently, new methods for the causal inference in the observational studies have been proposed such as the matching with the propensity score or the inverse probability treatment weighting. They have focused on how to control the confounders and how to evaluate the effect of the treatment on the result variable. However, these conventional methods are valid only when the treatment variable is categorical and both of the treatment and the result variables are directly observable. Research on the causal inference can be challenging in part because it may not be possible to directly observe the treatment and/or the result variable. To address this difficulty, we propose a method for estimating the average causal effect when both of the treatment and the result variables are latent. The latent class analysis has been applied to calculate the propensity score for the latent treatment variable in order to estimate the causal effect on the latent result variable. In this work, we investigate the causal effect of adolescents delinquency on their substance use using data from the 'National Longitudinal Study of Adolescent Health'.

• Tuberculosis and Respiratory Diseases
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• v.83 no.4
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• pp.268-275
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• 2020

Background: It is not evident that the attributable risk of smoking on mortality in Korea has decreased. We investigated the impact of smoking on all-cause mortality and estimated the attributable risk of smoking in Korean adults. Methods: Those aged ≥20 years with smoking history in the Korean National Health and Nutrition Examination Surveys (KNHANES) 2007-2015 were enrolled. We categorized the participants into three groups as follows: never smoker, <20 pack-years (PY) smokers, and ≥20 PY smokers. We applied inverse probability weighting using propensity scores to control various confounders between the groups. All-cause mortality risks were compared between the groups using the Kaplan-Meier log-rank test. The effects of smoking-attributable risks (ARs) on mortality were also calculated. Results: A total of 50,458 participants were included. Among them, 19,334 (38.3%) were smokers and 31,124 (61.7%) were never smokers. Those with a smoking history of 20 PY or more (≥20 PY smokers), those with a smoking history of less than 20 PY (<20 PY smokers), and never smokers were 18.1%, 20.2%, and 61.7%, respectively, of the study population. Smokers had a higher risk of all-cause mortality compared to never smokers (log-rank test p<0.01). The ARs of smoking were 21.8% (95% confidence interval [CI], 5.7%-37.9%) and 9.0% (95% CI, 6.1%-12.0%) in males and females, respectively. ARs decreased from 24.2% to 19.5% in males and from 9.5% to 4.1% in females between 2007-2010 and 2011-2015. Conclusion: Our study using KNHANES IV-VI data demonstrated that smoking increased the risk of all-cause mortality in a dose-response manner and the ARs of smoking on mortality were 21.8% in males and 9.0% in females during 2007-2015. This suggests that the ARs of smoking on mortality have decreased since around 2010.