Influence analyses of nonlinear mixed-effects models

Nonlinear mixed-effects models are very useful in analyzing repeated-measures data and have received a lot of attention in the field. In this paper, we propose a method to detect influential observations in such models, on the basis of the maximum likelihood estimates that are obtained by a stochastic approximation algorithm with Markov chain Monte Carlo method. The development utilizes the data augmentation technique that treats the random effects as missing data, and considers the conditional expectation of the complete-data log-likelihood function relating to an EM algorithm. Diagnostic measures are derived from the case-deletion approach and the local influence approach, and are approximated by a large sample of random effects that are simulated from the appropriate conditional distributions by a Metropolis-Hastings algorithm. Results obtained from two illustrative examples are reported. (C) 2002 Elsevier B.V. All rights reserved.