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http://hdl.handle.net/1942/2056
Title: | A protective estimator for longitudinal binary data subject to non-ignorable non-monotone missingness | Authors: | Fitzmaurice, Garrett M. Lipsitz, Stuart R. MOLENBERGHS, Geert IBRAHIM, Joseph |
Issue Date: | 2005 | Publisher: | BLACKWELL PUBLISHING | Source: | JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES A-STATISTICS IN SOCIETY, 168. p. 723-735 | Abstract: | 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. | Notes: | Harvard Univ, Sch Publ Hlth, Dept Biostat, Boston, MA 02115 USA. Brigham & Womens Hosp, Boston, MA 02115 USA. Med Univ S Carolina, Charleston, SC 29425 USA. Limburgs Univ Centrum, Diepenbeek, Belgium. Univ N Carolina, Chapel Hill, NC 27515 USA.Fitzmaurice, GM, Harvard Univ, Sch Publ Hlth, Dept Biostat, 655 Huntington Ave, Boston, MA 02115 USA.fitzmaur@hsph.harvard.edu | Keywords: | incomplete data; maximum likelihood; repeated measurements; sensitivity analysis;incomplete data; maximum likelihood; repeated measurements; sensitivity analysis | Document URI: | http://hdl.handle.net/1942/2056 | ISSN: | 0964-1998 | e-ISSN: | 1467-985X | DOI: | 10.1111/j.1467-985X.2005.00374.x | ISI #: | 000232168800006 | Rights: | (C) 2005 Royal Statistical Society | Category: | A1 | Type: | Journal Contribution | Validations: | ecoom 2006 |
Appears in Collections: | Research publications |
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