ANALYSIS OF REPEATED MEASUREMENT DATA USING THE NONLINEAR MIXED EFFECTS MODEL

Situations in which repeated measurements are taken on each of several individual items arise in many areas. These include assay development , where concentration-response data are available for each assay run i n a series of assay experiments; pharmacokinetic analysis, where repea ted blood concentration measurements are obtained from each of several subjects; and growth or decay studies, where growth or decay are meas ured over time for each plant, animal, or some other experimental unit . In these situations the model describing the response is often nonli near in the parameters to be estimated, as is the case for the four-pa rameter logistic model, which is frequently used to characterize conce ntration-response relationships for radioimmunoassay enzyme-linked imm unosorbent assay. Furthermore, response variability typically increase s with level of response. The objectives of an analysis vary according to the application: for assay analysis, calibration of unknowns for t he most recent run may be of interest; in pharmacokinetics, characteri zation of drug disposition for a patient population may be the focus. The nonlinear mixed effects (NME) model has been used to describe repe ated measurement data for which the mean response function is nonlinea r. In this tutorial, the NME model is motivated and described, and sev eral methods are given for estimation and inference in the context of the model. The methods are illustrated by application to examples from the fields of water transport kinetics, assay development, and pharma cokinetics.