A Bayesian sensitivity analysis of the effect of different random effects distributions on growth curve models

Abstract

Growth curve data consist of repeated measurements of a continuous growth process of human, animal, plant, microbial or bacterial genetic data over time in a population of individuals. A classical approach for analyzing such data is the use of non-linear mixed effects models under normality assumption for the responses. But, sometimes the underlying population that the sample is extracted from is an abnormal population or includes some homogeneous sub-samples. So, detection of original properties of the population is an important scientific question of interest. (To be continued on page 2388).

Key words: Bayesian paradigm; Dirichlet process; growth curve models; mixed effects model; repeated measurements data; sensitivity analysis.