Local Influence Diagnostics for Nonlinear Mixed Models under the Case-Weight Perturbation Scheme in SAS

Abstract

The nonlinear mixed model is a popular tool for analyzing continuous longitudinal data. This paper is primarily concerned with gauging the sensitivity of nonlinear mixed models to influential observations through local influence, which assesses the impact of small perturbations of the likelihood function. Unlike when case deletion is used, in local influence the model only needs to be fitted once, making it much more computationally appealing. The methodology is illustrated with two datasets, establishing that the local influence diagnostic can easily be applied to nonlinear mixed models through the NLMIXED procedure in the SAS software as a tool to identify influential individuals.