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A case study of competing risk analysis in the presence of missing data 원문보기

Communications for statistical applications and methods = 한국통계학회논문집, v.30 no.1, 2023년, pp.1 - 19  

Limei Zhou (Institute for Clinical Evaluative Sciences (ICES)) ,  Peter C. Austin (Institute for Clinical Evaluative Sciences (ICES)) ,  Husam Abdel-Qadir (Institute for Clinical Evaluative Sciences (ICES))

Abstract AI-Helper 아이콘AI-Helper

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 efficiency. In recent years propensity score (PS) matching has been incre...

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참고문헌 (24)

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