Doubly Robust Imputation of Incomplete Binary Longitudinal DataShahab Jolani1 and Stef van Buuren1,2*
- *Corresponding Author:
- Stef van Buuren
Department of Methodology and Statistics, Utrecht University
Utrecht, The Netherlands
Tel: 31 30 253 5194
E-mail: [email protected]
Received date: March 17 2014; Accepted date: May 06, 2014; Published date: May 12, 2014
Citation: Jolani S, van Buuren S (2014) Doubly Robust Imputation of Incomplete Binary Longitudinal Data. J Biomet Biostat 5: 194 doi: 10.4172/2155-6180.1000194
Copyright: © 2014 Jolani S, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are are credited.
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.