Evaluation of Multiple Imputation for Longitudinal Ordinal Data under MCAR and MAR Missing-Data Mechanisms
學年 99
學期 2
出版（發表）日期 2011-06-01
作品名稱 Evaluation of Multiple Imputation for Longitudinal Ordinal Data under MCAR and MAR Missing-Data Mechanisms
作品名稱（其他語言）
著者 Tuan, Li-Wen; Chen, Yi-Ju; Li, Pai-Ling; Lin, Kuo-Chin
單位 淡江大學統計學系
出版者 Toroku: ICIC International
著錄名稱、卷期、頁數 ICIC Express Letters 5(6), pp.1833-1838
摘要 Multiple imputation can be used to solve the problem of missing data that is a common occurrence in longitudinal studies. An imputation strategy proposed by Demirtas and Hedeker (Statistics in Medicine 2008; 27, 4086-4093) is to deal with incomplete longitudinal ordinal data, which converts discrete outcomes to continuous outcomes by generating normal values, employs multiple method based on normality, and reconverts to binary scale as well as ordinal one. The performance of multiple imputation in terms of standardized bias, root-mean-squared error and coverage percentage under missing completely at random (MCAR) and missing at random (MAR) was discussed by various configurations. The simulated results indicated this mutation strategy is suitable for most of incomplete data under these two missing-data mechanisms.
關鍵字 MAR; MCAR; Multiple imputation; Ordinal scale
語言 en
ISSN 1881-803X
期刊性質 國外
收錄於 EI
產學合作
通訊作者 Chen, Yi-Ju
審稿制度 是
國別 JPN
公開徵稿
出版型式 紙本
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