Drug risk assessment with determining the number of sub-populations under finite mixture normal models
In some biological experiments, it is quite common that laboratory subjects may be different in their patterns of susceptibility to a treatment. In these situations, finite mixture model analysis becomes a useful tool. Under a finite mixture normal model, drug risk assessment problems are studied under the assumption that the population at risk consists of multiple sub-types of different susceptibilities. The EM algorithm and its extension are utilized to obtain the maximum likelihood estimates of the model. Particular discussion is given to the model selection problem based on Bayes factor, which is approximated by the Schwarz criterion. Meantime, the practical significance of the proposed method is illustrated with an actual dose-response data set. (C) 2003 Elsevier B.V. All rights reserved.
risk assessment mixture normal models em algorithm model selection bayes factor schwarz criterion dose-response bic