An EM Algorithm for Fitting a 4-Parameter Logistic Model to Binary Dose-Response Data.

ABSTRACT: This article is motivated by the need of biological and environmental scientists to fit a popular nonlinear model to binary dose-response data. The 4-parameter logistic model, also known as the Hill model, generalizes the usual logistic regression model to allow the lower and upper response asymptotes to be greater than zero and less than one, respectively. This article develops an EM algorithm, which is naturally suited for maximum likelihood estimation under the Hill model after conceptualizing the problem as a mixture of subpopulations in which some subjects respond regardless of dose, some fail to respond regardless of dose, and some respond with a probability that depends on dose. The EM algorithm leads to a pair of functionally independent 2-parameter optimizations and is easy to pro

SUBMITTER: Dinse GE 

PROVIDER: S-EPMC3137126 | biostudies-literature | 2011 Jun

REPOSITORIES: biostudies-literature