• Title/Summary/Keyword: Propensity Score

Search Result 196, Processing Time 0.109 seconds

Performance study of propensity score methods against regression with covariate adjustment

  • Park, Jincheol
    • Journal of the Korean Data and Information Science Society
    • /
    • v.26 no.1
    • /
    • pp.217-227
    • /
    • 2015
  • In observational study, handling confounders is a primary issue in measuring treatment effect of interest. Historically, a regression with covariate adjustment (covariate-adjusted regression) has been the typical approach to estimate treatment effect incorporating potential confounders into model. However, ever since the introduction of the propensity score, covariate-adjusted regression has been gradually replaced in medical literatures with various balancing methods based on propensity score. On the other hand, there is only a paucity of researches assessing propensity score methods compared with the covariate-adjusted regression. This paper examined the performance of propensity score methods in estimating risk difference and compare their performance with the covariate-adjusted regression by a Monte Carlo study. The study demonstrated in general the covariate-adjusted regression with variable selection procedure outperformed propensity-score-based methods in terms both of bias and MSE, suggesting that the classical regression method needs to be considered, rather than the propensity score methods, if a performance is a primary concern.

A Study on Nonresponse Adjistment by Using Propensity Scores (성향점수를 이용한 무응답 보정 연구)

  • Lee, Kay-O
    • Survey Research
    • /
    • v.10 no.1
    • /
    • pp.169-186
    • /
    • 2009
  • The propensity score method is used to minimize the bias level in social survey, which comes from nonresponse. The theoretical concept and the background of the propensity score method is discussed first. The propensity score method was first applied in the epidemiology observational study. I have summarized the process of the three propensity score methods that were used to reduce estimation bias in this study. Matching by propensity score is applied to the relatively large control group. Subclassification has the advantage of using whole control group data and regression adjustment is applied to multiple covariates as well as propensity score of each unit is computable and usable. Lastly, the application procedures of propensity score method to reduce the nonresponse bias is suggested and its applicability to real situation is reviewed with the existing data.

  • PDF

On Logistic Regression Analysis Using Propensity Score Matching (성향점수매칭 방법을 사용한 로지스틱 회귀분석에 관한 연구)

  • Kim, So Youn;Baek, Jong Il
    • Journal of Applied Reliability
    • /
    • v.16 no.4
    • /
    • pp.323-330
    • /
    • 2016
  • Purpose: Recently, propensity score matching method is used in a large number of research paper, nonetheless, there is no research using fitness test of before and after propensity score matching. Therefore, comparing fitness of before and after propensity score matching by logistic regression analysis using data from 'online survey of adolescent health' is the main significance of this research. Method: Data that has similar propensity in two groups is extracted by using propensity score matching then implement logistic regression analysis on before and after matching separately. Results: To test fitness of logistic regression analysis model, we use Model summary, -2Log Likelihood and Hosmer-Lomeshow methods. As a result, it is confirmed that the data after matching is more suitable for logistic regression analysis than data before matching. Conclusion: Therefore, better result which has appropriate fitness will be shown by using propensity score matching shows better result which has better fitness.

A step-by-step guide to Propensity Score Matching method using R program in dental research (치의학 연구에서 R program을 이용한 성향점수매칭의 단계적 안내)

  • An, Hwayoen;Lim, Hoi-Jeong
    • The Journal of the Korean dental association
    • /
    • v.58 no.3
    • /
    • pp.152-168
    • /
    • 2020
  • The propensity score matching method is a statistical method used to reduce selection bias in observational studies and to show effects similar to random allocation. There are many observational studies in dentistry research, and differences in baseline covariates between the control and case groups affect the outcome. In order to reduce the bias due to confounding variables, the propensity scores are used by equating groups based on the baseline covariates. This method is effective, especially when there are many covariates or the sample size is small. In this paper, the propensity score matching method was explained in a simple way with a dental example by using R software. This simulated data were obtained from one of retrospective study. The control group and the case group were matched according to the propensity score and compared before and after treatment. The propensity score matching method could be an alternative to compensate for the disadvantage of the observation study by reducing the bias based on the covariates with the propensity score.

  • PDF

Strengthening Causal Inference in Studies using Non-experimental Data: An Application of Propensity Score and Instrumental Variable Methods (비실험자료를 이용한 연구에서 인과적 추론의 강화: 성향점수와 도구변수 방법의 적용)

  • Kim, Myoung-Hee;Do, Young-Kyung
    • Journal of Preventive Medicine and Public Health
    • /
    • v.40 no.6
    • /
    • pp.495-504
    • /
    • 2007
  • 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.

The effect of ability grouping on Mathematics achievement - Utilizing the Propensity Score Matching - (수준별 이동수업이 고등학생의 수학 성취도에 미치는 영향에 대한 연구 - 경향점수매칭법(Propensity Score Matching)을 활용하여 -)

  • Hong, Soon Sang;Lee, Deok Ho
    • Journal of the Korean School Mathematics Society
    • /
    • v.18 no.1
    • /
    • pp.149-167
    • /
    • 2015
  • In this study, we estimate the effect of ability grouping on mathematics achievement empirically. We use propensity score matching(PSM) method to minimize selection bias and estimate the effect of ability grouping on the mathematics standard score of Scholastic Ability Test with the KELS(Korea Education Longitudinal Study) 6th stage data. The result indicated that relationship between ability grouping and mathematics achievement is positive and Policy efforts is needed to operate ability grouping effectively.

Bootstrap estimation of the standard error of treatment effect with double propensity score adjustment (이중 성향점수 보정 방법을 이용한 처리효과 추정치의 표준오차 추정: 붓스트랩의 적용)

  • Lim, So Jung;Jung, Inkyung
    • The Korean Journal of Applied Statistics
    • /
    • v.30 no.3
    • /
    • pp.453-462
    • /
    • 2017
  • Double propensity score adjustment is an analytic solution to address bias due to incomplete matching. However, it is difficult to estimate the standard error of the estimated treatment effect when using double propensity score adjustment. In this study, we propose two bootstrap methods to estimate the standard error. The first is a simple bootstrap method that involves drawing bootstrap samples from the matched sample using the propensity score as well as estimating the standard error from the bootstrapped samples. The second is a complex bootstrap method that draws bootstrap samples first from the original sample and then applies the propensity score matching to each bootstrapped sample. We examined the performances of the two methods using simulations under various scenarios. The estimates of standard error using the complex bootstrap were closer to the empirical standard error than those using the simple bootstrap. The simple bootstrap methods tended to underestimate. In addition, the coverage rates of a 95% confidence interval using the complex bootstrap were closer to the advertised rate of 0.95. We applied the two methods to a real data example and found also that the estimate of the standard error using the simple bootstrap was smaller than that using the complex bootstrap.

A Comparison for Constitutional Body Type between Korean and Chinese-Korean - Using Propensity Score Matching - (한국인과 연길 거주 조선족의 체질별 체형 비교 연구(Propensity Score Matching을 활용하여))

  • Kim, Hoseok;Baek, Younghwa;Lee, Siwoo;Yoo, Jonghyang
    • Journal of Sasang Constitutional Medicine
    • /
    • v.28 no.1
    • /
    • pp.11-18
    • /
    • 2016
  • Objectives The body shape was a key information for diagnosing the Sasang constitution (SC) in Sasang constitutional medicine. The aim of this study was to compare the body shapes and mainly focusing on the Korean and Chinese-KoreanMethods We calculated the propensity score for each SC type in males and females separately, and compared body shape including 8 circumference and 5 width between Korean and Chinese-Korean according to the sex and SC.Results Koreans have larger trunk and hip area compared to Chinese-Koreans, while Chinese-Koreans have larger abdomen compared with Koreans. Most variables were significantly different among SC types, for both Korean and Chinese-Korean. Especially, the Taeumin (TE) type has the largest body shape compared with the other SC types, it was similar between Korean and Chinese-Korean.Conclusions This study showed that the TE type has the largest body shape, followed by Soyangin (SY) and Soeumin (SE) in order, for both Korean and Chinese-Korean respectively. These results suggests that the body shape of Chinese-Korean is similar with Korean based on SC type.

Difference in Healthcare Utilization for Percutaneous Transluminal Coronary Angioplasty Inpatients by Insurance Types: Propensity Score Matching Analysis (의료보장유형에 따른 Percutaneous Transluminal Coronary Angioplasty 입원 환자의 의료이용 차이 분석: Propensity Score Matching을 이용하여)

  • Seo, Eun-Won;Lee, Kwang-Soo
    • Health Policy and Management
    • /
    • v.25 no.1
    • /
    • pp.3-10
    • /
    • 2015
  • Background: Previous studies showed differences in healthcare utilization among insurance types. This study aimed to analyze the difference in healthcare utilization for percutaneous transluminal coronary angioplasty inpatients by insurance types after controlling factors affecting healthcare utilization using propensity score matching (PSM). Methods: The 2011 national inpatient sample based on health insurance claims data was used for analysis. PSM was used to control factors influencing healthcare utilization except insurance types. Length of stay and total charges were used as healthcare utilization variables. Patients were divided into National Health Insurance (NHI) and Medical Aid (MA) patients. Factors representing inpatients (gender, age, admission sources, and Elixhauser comorbidity index) and hospitals (number of doctors, number of beds, and location of hospitals) were used as covariates in PSM. Results: Tertiary hospitals didn't show significant difference in length of stay and total charges after PSM between two insurance types. However, MA patients showed significantly longer length of stay than that of NHI patients after PSM in general hospitals. Multivariate regression analysis provided that admission sources, Elixhauser comorbidity index, insurance types, number of doctors, and location of hospitals (province) had significant influences on the length of stay in general hospitals. Conclusion: Study results provided evidences that healthcare utilization was differed by insurance types in general hospitals. Health policy makers will need to prepare interventions to influence the healthcare utilization differences between insurance types.

Pure additive contribution of genetic variants to a risk prediction model using propensity score matching: application to type 2 diabetes

  • Park, Chanwoo;Jiang, Nan;Park, Taesung
    • Genomics & Informatics
    • /
    • v.17 no.4
    • /
    • pp.47.1-47.12
    • /
    • 2019
  • The achievements of genome-wide association studies have suggested ways to predict diseases, such as type 2 diabetes (T2D), using single-nucleotide polymorphisms (SNPs). Most T2D risk prediction models have used SNPs in combination with demographic variables. However, it is difficult to evaluate the pure additive contribution of genetic variants to classically used demographic models. Since prediction models include some heritable traits, such as body mass index, the contribution of SNPs using unmatched case-control samples may be underestimated. In this article, we propose a method that uses propensity score matching to avoid underestimation by matching case and control samples, thereby determining the pure additive contribution of SNPs. To illustrate the proposed propensity score matching method, we used SNP data from the Korea Association Resources project and reported SNPs from the genome-wide association study catalog. We selected various SNP sets via stepwise logistic regression (SLR), least absolute shrinkage and selection operator (LASSO), and the elastic-net (EN) algorithm. Using these SNP sets, we made predictions using SLR, LASSO, and EN as logistic regression modeling techniques. The accuracy of the predictions was compared in terms of area under the receiver operating characteristic curve (AUC). The contribution of SNPs to T2D was evaluated by the difference in the AUC between models using only demographic variables and models that included the SNPs. The largest difference among our models showed that the AUC of the model using genetic variants with demographic variables could be 0.107 higher than that of the corresponding model using only demographic variables.