Fitting nonlinear and constrained generalized estimating equations with optimization software

Publisher:
International Biometric Soc
Publication Type:
Journal Article
Citation:
Biometrics, 2000, 56 (4), pp. 1268 - 1271
Issue Date:
2000-01
Full metadata record
Files in This Item:
Filename Description Size
Thumbnail2011008571OK.pdf745.76 kB
Adobe PDF
In this article, we present an estimation approach for solving nonlinear constrained generalized estimating equations that can be implemented using object-oriented software for nonlinear programming, such as nlminb in Splus or fmincon and Isqnonlin in Matlab. We show how standard estimating equation theory includes this method as a special case so that our estimates, when unconstrained, will remain consistent: and asymptotically normal. To illustrate this method, we fit a nonlinear dose-response model with nonnegative mixed bound constraints to clustered binary data from a developmental toxicity study. Satisfactory confidence intervals are found using a nonparametric bootstrap method when a common correlation coefficient is assumed for all the dose groups and for some of the dose-specific groups.
Please use this identifier to cite or link to this item: