Generalized linear mixture models for handling nonignorable dropouts in longitudinal studies

G M Fitzmaurice; N M Laird

doi:10.1093/biostatistics/1.2.141

Generalized linear mixture models for handling nonignorable dropouts in longitudinal studies

Biostatistics. 2000 Jun;1(2):141-56. doi: 10.1093/biostatistics/1.2.141.

Authors

G M Fitzmaurice¹, N M Laird

Affiliation

¹ Department of Biostatistics, Harvard School of Public Health, 655 Huntington Avenue, Boston, MA 02115, USA. fitzmaur@hsph.harvard.edu

PMID: 12933516
DOI: 10.1093/biostatistics/1.2.141

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.