Imputation Strategies for Incomplete Longitudinal Binary Data

• Main Authors: Tzu-Ying Li, 李紫熒
• Other Authors: 陳怡如
• Format: Others
• Language: en_US
• Published: 2011
• Online Access: http://ndltd.ncl.edu.tw/handle/07067288866067263662

碩士 === 淡江大學 === 統計學系碩士班 === 99 === It is very common for longitudinal studies to involve missing data. The imputation method is one of the effective procedures for handling with the problem of missing data. Based on the well-developed multiple imputation for normal responses and a random number generation algorithm for binary outcomes, Demirtas and Hedeker (2007) introduced a quasi-imputation strategy for incomplete longitudinal binary data. The shortcomings of Demirtas-Hedeker approach are that positive-definiteness of the correlation matrix cannot be guaranteed and the correlations need to satisfy the constraint for a unique solution. To improve the shortcomings of Demirtas-Hedeker method, the proposed methods can be regarded as the modification of Demirtas-Hedeker method with simpler procedures. The performance of Demirtas-Hedeker method and the proposed procedures is compared in terms of standardized bias, coverage percentage, and root-mean-squared error under various configurations of missing rates and missingness mechanisms. A real data set is used to illustrate the application of the proposed methods.