首页> European journal of epidemiology> A comparison of different methods to handle missing data in the context of propensity score analysis.

A comparison of different methods to handle missing data in the context of propensity score analysis.

导出 OA

翻译打印收藏纠错

第一作者： Jungyeon,Choi

第一单位： Department of Clinical Epidemiology, Leiden University Medical Center, Albinusdreef 2, C7-P, 2333 ZA, Leiden, The Netherlands. J.Choi@lumc.nl.

作者： Jungyeon,Choi ^[1] ; Olaf M,Dekkers ^[2] ; Saskia,le Cessie ^[3]

作者单位： Department of Clinical Epidemiology, Leiden University Medical Center, Albinusdreef 2, C7-P, 2333 ZA, Leiden, The Netherlands. J.Choi@lumc.nl. ^[1] Department of Clinical Epidemiology, Leiden University Medical Center, Albinusdreef 2, C7-P, 2333 ZA, Leiden, The Netherlands.;Department of Endocrinology and Metabolism, Leiden University Medical Center, Albinusdreef 2, C7-P, 2333 ZA, Leiden, The Netherlands. ^[2] Department of Clinical Epidemiology, Leiden University Medical Center, Albinusdreef 2, C7-P, 2333 ZA, Leiden, The Netherlands.;Department of Biomedical Data Sciences, Leiden University Medical Center, Albinusdreef 2, C7-P, 2333 ZA, Leiden, The Netherlands. ^[3]

关键词 Effect modification Missing data Missing indicator Missingness graph Multiple imputation Propensity score analysis

医学主题词队列研究(Cohort Studies);计算机模拟(Computer Simulation);数据说明, 统计(Data Interpretation, Statistical);人类(Humans);模型, 统计学(Models, Statistical);倾向性评分(Propensity Score);统计学(主题)(Statistics as Topic)

DOI 10.1007/s10654-018-0447-z

PMID 30341708

发布时间 2020-02-25

翻译满意度评价：

准确一般字义偏差语意偏离译文作用小提交