Generalized Linear Mixture Models for Handling Nonignorable Dropouts in Longitudinal Studies

by Garrett M. Fitzmaurice (1) and Nan M. Laird (1)
(1) Department of Biostatistics, Harvard School of Public Health, 655 Huntington Avenue, Boston, MA 02115, USA fitzmaur@hsph.harvard.edu

Abstract
This paper presents a method for analysing longitudinal data when there are dropouts. In particular, we develop a simple method based on generalized linear mixture models for handling nonignorable dropouts for a variety of discrete and continuous outcomes. Statistical inference for the model parameters is based on a generalized estimating equations (GEE) approach (Liang and Zeger, 1986). The proposed method yields estimates of the model parameters that are valid when nonresponse is nonignorable under a variety of assumptions concerning the dropout process. Furthermore, the proposed method can be implemented using widely available statistical software. Finally, an example using data from a clinical trial of contracepting women is used to illustrate the methodology.

Keywords: Discrete data; Generalized estimating equations; Missing data; Nonresponse; Repeated measures

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