Mixed-effects nonlinear regression for unbalanced repeated measures

Abstract

Repeated measures data, such as clinical pharmacokinetic data, growth data, and dose-response data, are often inherently nonlinear with respect to a given response function and are frequently incomplete and/or unbalanced. Nonlinear random-effects models together with a variety of estimation procedures have been proposed for the analysis of such data. This paper is concerned with a straightforward procedure for estimating and comparing the parameters of a generalized mixed-effects nonlinear regression model. The asymptotic properties of the proposed estimators are given and large-sample tests of hypothesis provided. The results are applied to in vitro data on the water transport kinetics of hemodialyzers used in the treatment of patients with chronic renal failure.