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A protective estimator for longitudinal binary data subject to non-ignorable non-monotone missingness

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In longitudinal studies missing data are the rule not the exception. We consider the analysis of longitudinal binary data with non-monotone missingness that is thought to be non-ignorable. In this setting a full likelihood approach is complicated algebraically and can be computationally prohibitive when there are many measurement occasions. We propose a ‘protective’ estimator that assumes that the probability that a response is missing at any occasion depends, in a completely unspecified way, on the value of that variable alone. Relying on this ‘protectiveness’ assumption, we describe a pseudolikelihood estimator of the regression parameters under non-ignorable missingness, without having to model the missing data mechanism directly. The method proposed is applied to CD4 cell count data from two longitudinal clinical trials of patients infected with the human immunodeficiency virus.

Keywords: Incomplete data; Maximum likelihood; Repeated measurements; Sensitivity analysis

Document Type: Research Article


Affiliations: 1: Brigham and Women's Hospital, Boston, and Harvard School of Public Health, Boston, USA 2: Medical University of South Carolina, Charleston, USA 3: Limburgs Universitair Centrum, Diepenbeek, Belgium 4: University of North Carolina, Chapel Hill, USA

Publication date: November 1, 2005

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