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A Bivariate Pseudolikelihood for Incomplete Longitudinal Binary Data with Nonignorable Nonmonotone Missingness
Author(s) -
Sinha Sanjoy K.,
Troxel Andrea B.,
Lipsitz Stuart R.,
Sinha Debajyoti,
Fitzmaurice Garrett M.,
Molenberghs Geert,
Ibrahim Joseph G.
Publication year - 2011
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/j.1541-0420.2010.01525.x
Subject(s) - missing data , estimator , bivariate analysis , independence (probability theory) , computer science , parametric statistics , binary number , mathematics , statistics , arithmetic
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.