The Performance of Multiple Imputation for Longitudinal Ordinal Data under MCAR and MAR Dropouts

• Main Authors: Li-Wen Tuan, 段力文
• Other Authors: Yi-Ju Chen
• Format: Others
• Language: en_US
• Published: 2010
• Online Access: http://ndltd.ncl.edu.tw/handle/38793144371710525199

碩士 === 淡江大學 === 統計學系碩士班 === 98 === Missing data are a common occurrence in longitudinal studies. Multiple imputation can be used to solve the problem of missing data. Since the current imputation methods are developed based on the normality, Demirtas and Hedeker (2008) proposed a multiple imputation strategy for incomplete longitudinal ordinal data, which converts discrete scale to continuous scale by generating normal outcomes and reconvert to binary scale as well as ordinal one after filling in multiple imputed values. The primary purpose of this article is to evaluate the performance of Demirtas and Hedeker’s method in terms of standardized bias, coverage percentage and root-mean-squared error under various missing mechanisms such as missing completely at random (MCAR) and missing at random (MAR). According to the simulated results, the plausibility of this imputation strategy is appropriate for analyzing incomplete longitudinal ordinal data under these two missing mechanisms.