개인구독
소속 기관이 없으신 경우, 개인 정기구독을 하시면 저렴하게
논문을 무제한 열람 이용할 수 있어요.
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 한국통계학회 CSAM(Communications for Statistical Applications and Methods) CSAM(Communications for Statistical Applications and Methods) 제30권 제1호
- 발행연도
- 2023.1
- 수록면
- 1 - 19 (19page)
이용수
초록· 키워드
오류제보하기
Observational data with missing or incomplete data are common in biomedical research. Multiple imputation is an effective approach to handle missing data with the ability to decrease bias while increasing statistical power and effciency. In recent years propensity score (PS) matching has been increasingly used in observational studies to estimate treatment effect as it can reduce confounding due to measured baseline covariates. In this paper, we describe in detail approaches to competing risk analysis in the setting of incomplete observational data when using PS matching. First, we used multiple imputation to impute several missing variables simultaneously, then conducted propensity-score matching to match statin-exposed patients with those unexposed. Afterwards, we assessed the effect of statin exposure on the risk of heart failure-related hospitalizations or emergency visits by estimating both relative and absolute effects. Collectively, we provided a general methodological framework to assess treatment effect in incomplete observational data. In addition, we presented a practical approach to produce overall cumulative incidence function (CIF) based on estimates from multiple imputed and PS-matched samples.
목차
Abstract
1. Introduction
2. Methodology
3. Statistical analysis
4. Results
5. Discussion
References
참고문헌 (0)
참고문헌 신청함께 읽어보면 좋을 논문
논문 유사도에 따라 DBpia 가 추천하는 논문입니다. 함께 보면 좋을 연관 논문을 확인해보세요!
-
Multiple imputation for competing risks survival data via pseudo-observations
CSAM(Communications for Statistical Applications and Methods)
2018 .07
-
특성도를 이용한 결측치 대체방법
응용통계연구
2017 .06
-
Two-stage imputation method to handle missing data for categorical response variable
CSAM(Communications for Statistical Applications and Methods)
2023 .11
-
A comparison of imputation methods using machine learning models
CSAM(Communications for Statistical Applications and Methods)
2023 .05
-
Identification of Differentially Expressed Genes Using Tests Based on Multiple Imputations
Quantitative Bio-Science
2017 .05
-
A Study on Imputation for Missing Data using the Kriging
Journal of The Korean Data Analysis Society
2015 .01
-
Comparison of missing data methods in clustered survival data using Bayesian adaptive B-Spline estimation
CSAM(Communications for Statistical Applications and Methods)
2018 .03
-
Application of imputation methods with adjusting multi-center effect on Crohns disease data in Korea
한국데이터정보과학회지
2018 .03
-
임상시험에서 이분형 결측치 처리방법의 비교연구
응용통계연구
2016 .04
-
사망원인이 결측된 결장암 3기 자료 분석에 관한 연구
한국데이터정보과학회지
2021 .01
-
군집화 및 특성도를 이용한 결측치 대체 방법
응용통계연구
2018 .02
-
Large tests of independence in incomplete two-way contingency tables using fractional imputation
한국데이터정보과학회지
2015 .08
-
missForest with feature selection using binary particle swarm optimization improves the imputation accuracy of continuous data
Genes & Genomics
2022 .06
-
불균형 두 집단의 매칭방법 제안
한국데이터정보과학회지
2018 .05
-
비선형 모델을 이용한 결측 대체 방법 비교
응용통계연구
2019 .08
-
Iterative integrated imputation for missing data and pathway models with applications to breast cancer subtypes
CSAM(Communications for Statistical Applications and Methods)
2019 .07
-
Regression analysis of interval censored competing risk data using a pseudo-value approach
CSAM(Communications for Statistical Applications and Methods)
2016 .11
-
측정오차로 인한 불완전한 측정 하에서의 결측값 대체
Journal of The Korean Data Analysis Society
2021 .10
-
한계와 이상치가 있는 결측치의 로버스트 다중대체 방법
응용통계연구
2019 .12
-
시간경로 마이크로어레이 자료에서 k-최근접 이웃과 패턴일치지수를 이용한 결측치 대체방법
한국데이터정보과학회지
2018 .09
이 논문의 저자 정보
최근 본 자료
댓글(0)
0
UCI(KEPA) : I410-ECN-0101-2023-310-001433220