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|Title: ||A Bivariate Pseudolikelihood for Incomplete Longitudinal Binary Data with Nonignorable Nonmonotone Missingness|
|Authors: ||Sinha, Sanjoy K.|
Troxel, Andrea B.
Lipsitz, Stuart R.
Fitzmaurice, Garrett M.
Ibrahim, Joseph G.
|Issue Date: ||2011|
|Citation: ||BIOMETRICS, 67(3). p. 1119-1126|
|Abstract: ||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.|
|Notes: ||[Sinha, SK] Carleton Univ, Sch Math & Stat, Ottawa, ON K1S 5B6, Canada [Troxel, AB] Univ Penn, Sch Med, Philadelphia, PA 19104 USA [Lipsitz, SR; Fitzmaurice, GM] Harvard Univ, Sch Med, Boston, MA 02115 USA [Sinha, D] Florida State Univ, Tallahassee, FL 32306 USA [Molenberghs, G] Hasselt Univ, B-3590 Diepenbeek, Belgium [Ibrahim, JG] Univ N Carolina, Chapel Hill, NC 27599 USA
|ISI #: ||000294866800046|
|Type: ||Journal Contribution|
|Validation: ||ecoom, 2012|
|Appears in Collections: ||Research publications|
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