Bivariate Pseudolikelihood for Incomplete Longitudinal Binary Data with Nonignorable Nonmonotone Missingness

• Published in: Biometrics Vol. 67; no. 3; pp. 1119 - 1126
• Main Authors: Sinha, Sanjoy K., Troxel, Andrea B., Lipsitz, Stuart R., Sinha, Debajyoti, Fitzmaurice, Garrett M., Molenberghs, Geert, Ibrahim, Joseph G.
• Format: Journal Article
• Language: English
• Published: Malden, USA Blackwell Publishing Inc 01.09.2011; Wiley-Blackwell; Blackwell Publishing Ltd
• Subjects: AIDS; Binary data; Binary system; BIOMETRIC PRACTICE; Biometrics; biometry; Biometry - methods; CD4 Lymphocyte Count; Clinical trials; Computer Simulation; Data analysis; Data Interpretation, Statistical; Estimation bias; Estimators; HIV; HIV infections; HIV Infections - diagnosis; Human immunodeficiency virus; Humans; Likelihood Functions; Logistic regression; Longitudinal data; Longitudinal Studies - statistics & numerical data; Marginal model; Maximum likelihood; Maximum likelihood estimation; Missing data; Missing data mechanism; Modeling; Models, Statistical; patients; Sample size
• ISSN: 0006-341X; 1541-0420; 1541-0420
• DOI: 10.1111/j.1541-0420.2010.01525.x

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.