Doubly Robust Imputation of Incomplete Binary Longitudinal Data

Journal of Biometrics & Biostatistics

article

Shahab Jolani¹  Stef van Buuren¹ 

[1] Department of Methodology and Statistics, Utrecht University;Department of Statistics

关键词: Double protection;    Incomplete data;    Ignorable missingness;    Multiple imputation;   

DOI  :  10.4172/2155-6180.1000194

来源: Hilaris Publisher

【 摘 要 】

Estimation in binary longitudinal data by using generalized estimating equation (GEE) becomes complicated in the presence of missing data because standard GEEs are only valid under the restrictive missing completely at random assumption. Weighted GEE has therefore been proposed to allow the validity of GEE's under the weaker missing at random assumption. Multiple imputation offers an attractive alternative, by which the incomplete data are pre-processed, and afterwards the standard GEE can be applied to the imputed data. Nevertheless, the imputation methodology requires correct specification of the imputation model. Dual imputation method provides a new way to increase the robustness of imputations with respect to model misspecification. The method involves integrating the so-called doubly robust ideas into the imputation model. Focusing on incomplete binary longitudinal data, we combine DIM and GEE (DIM-GEE) and study the relative performance of the new method in a case study of obesity among children, as well as a simulation study.

【 授权许可】

Unknown