Evaluation of Multiple Imputation for Longitudinal Ordinal Data under MCAR and MAR Missing-Data Mechanisms

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Title:	Evaluation of Multiple Imputation for Longitudinal Ordinal Data under MCAR and MAR Missing-Data Mechanisms
Authors:	Tuan, Li-Wen;Chen, Yi-Ju;Li, Pai-Ling;Lin, Kuo-Chin
Contributors:	淡江大學統計學系
Keywords:	MAR;MCAR;Multiple imputation;Ordinal scale
Date:	2011-06
Issue Date:	2011-10-01 01:10:54 (UTC+8)
Publisher:	Toroku: ICIC International
Abstract:	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.
Relation:	ICIC Express Letters 5(6), pp.1833-1838
Appears in Collections:	[Graduate Institute & Department of Statistics] Journal Article

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