Simultaneous estimation of parameters in the bivariate Emax model
(English)Manuscript (preprint) (Other academic)
In this paper we explore inference in multi-response, nonlinear models. By multi-responsewe mean models with m>1 response variables and accordingly m relations. Each parameter/explanatory variable may appear in one or more of the relations. We study a system estimation approach for simultaneous computation and inference of the model and (co)variance parameters. For illustration we fit a bivariate Emax model to diabetes dose response data. Further the bivariate Emax model is used in a simulation study that compares the system estimation approach to equation-by-equation estimation. We conclude that overall the system estimation approach performs better for the bivariate Emax model when there are dependencies among relations. The stronger the dependencies the more we gain in precision by using system estimation rather than equation-by-equation estimation.
multi-response nonlinear models; system estimation; clinical trials; Emax model
Probability Theory and Statistics
Research subject Statistics
IdentifiersURN: urn:nbn:se:su:diva-102866OAI: oai:DiVA.org:su-102866DiVA: diva2:713851