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Biostatistics Advance Access published online on June 20, 2006
Biostatistics, doi:10.1093/biostatistics/kxl010
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© The Author 2006. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oxfordjournals.org
Received November 29, 2004
Revised June 9, 2006
Accepted June 16, 2006

Article

A Marginalized Pattern-Mixture Model for Longitudinal Binary Data when Non-Response Depends on Unobserved Responses

Kenneth J. Wilkins 1 and Garrett M. Fitzmaurice 2 *

1 Department of Biostatistics, Harvard School of Public Health 655 Huntington Avenue, Boston, MA 02115
2 Department of Biostatistics, Harvard School of Public Health 655 Huntington Avenue, Boston, MA 02115; Division of General Medicine, Brigham and Women's Hospital 1620 Tremont St., 3rd Fl, Boston, MA 02120-1613

* To whom correspondence should be addressed.
Garrett M. Fitzmaurice, E-mail: fitzmaur{at}hsph.harvard.edu


  Abstract

This paper proposes a method for modeling longitudinal binary data when non-response depends on unobserved responses. The proposed method presumes that the target of inference is the marginal distribution of the response at each occasion and its dependence on covariates, and can accommodate both monotone and non-monotone missingness. The approach involves a marginally specified pattern-mixture model that directly parameterizes both the marginal means at each occasion and the dependence of each response on indicators of non-response pattern. This formulation readily incorporates a variety of non-response processes assumed within a sensitivity analysis. Once identifying restrictions have been made, estimation of model parameters proceeds via solution to a set of modified generalized estimating equations. The proposed method provides an alternative to standard selection and pattern-mixture modeling frameworks, while featuring certain advantages of each. The paper concludes with application of the method to data from a contraceptive clinical trial with substantial dropout.

Keywords: Binary data; dropout; longitudinal method; marginal regression; missing data; non-response.
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