A Bivariate Pseudolikelihood for Incomplete Longitudinal Binary Data with Nonignorable Nonmonotone Missingness
Article first published online: 14 DEC 2010
© 2010, The International Biometric Society
Volume 67, Issue 3, pages 1119–1126, September 2011
How to Cite
Sinha, S. K., Troxel, A. B., Lipsitz, S. R., Sinha, D., Fitzmaurice, G. M., Molenberghs, G. and Ibrahim, J. G. (2011), A Bivariate Pseudolikelihood for Incomplete Longitudinal Binary Data with Nonignorable Nonmonotone Missingness. Biometrics, 67: 1119–1126. doi: 10.1111/j.1541-0420.2010.01525.x
- Issue published online: 14 SEP 2011
- Article first published online: 14 DEC 2010
- Received April 2010. Revised September 2010. Accepted September 2010.
- Logistic regression;
- Longitudinal data;
- Marginal model;
- Maximum likelihood;
- Missing data mechanism
Summary For analyzing longitudinal binary data with nonignorable and nonmonotone missing responses, a full likelihood method is complicated algebraically, and often requires intensive computation, especially when there are many follow-up times. As an alternative, a pseudolikelihood approach has been proposed in the literature under minimal parametric assumptions. This formulation only requires specification of the marginal distributions of the responses and missing data mechanism, and uses an independence working assumption. However, this estimator can be inefficient for estimating both time-varying and time-stationary effects under moderate to strong within-subject associations among repeated responses. In this article, we propose an alternative estimator, based on a bivariate pseudolikelihood, and demonstrate in simulations that the proposed method can be much more efficient than the previous pseudolikelihood obtained under the assumption of independence. We illustrate the method using longitudinal data on CD4 counts from two clinical trials of HIV-infected patients.