Doubly Robust Estimates for Binary Longitudinal Data Analysis with Missing Response and Missing Covariates
Article first published online: 31 JAN 2011
© 2011, The International Biometric Society
Volume 67, Issue 3, pages 830–842, September 2011
How to Cite
Chen, B. and Zhou, X.-H. (2011), Doubly Robust Estimates for Binary Longitudinal Data Analysis with Missing Response and Missing Covariates. Biometrics, 67: 830–842. doi: 10.1111/j.1541-0420.2010.01541.x
- Issue published online: 14 SEP 2011
- Article first published online: 31 JAN 2011
- Received January 2010. Revised October 2010. Accepted October 2010.
- Doubly robust;
- Estimating equation;
- Missing at random;
- Missing covariate;
- Missing response
Summary Longitudinal studies often feature incomplete response and covariate data. Likelihood-based methods such as the expectation–maximization algorithm give consistent estimators for model parameters when data are missing at random (MAR) provided that the response model and the missing covariate model are correctly specified; however, we do not need to specify the missing data mechanism. An alternative method is the weighted estimating equation, which gives consistent estimators if the missing data and response models are correctly specified; however, we do not need to specify the distribution of the covariates that have missing values. In this article, we develop a doubly robust estimation method for longitudinal data with missing response and missing covariate when data are MAR. This method is appealing in that it can provide consistent estimators if either the missing data model or the missing covariate model is correctly specified. Simulation studies demonstrate that this method performs well in a variety of situations.