title: Fitting Emax models to clinical trial dose-response data when the high dose asymptote is ill defined.
abstract: We consider fitting the so-called Emax model to continuous response data from clinical trials designed to investigate the dose-response relationship for an experimental compound. When there is insufficient information in the data to estimate all of the parameters because of the high dose asymptote being ill defined, maximum likelihood estimation fails to converge. We explore the use of either bootstrap resampling or the profile likelihood to make inferences about effects and doses required to give a particular effect, using limits on the parameter values to obtain the value of the maximum likelihood when the high dose asymptote is ill defined. The results obtained show these approaches to be comparable with or better than some others that have been used when maximum likelihood estimation fails to converge and that the profile likelihood method outperforms the method of bootstrap resampling used.
authors.affiliation: School of Mathematics, Statistics and Actuarial Science, University of Kent, Canterbury, Kent, UK.
authors.forename: P
authors.initials: P
authors.lastname: Brain
authors.forename: S
authors.initials: S
authors.lastname: Kirby
authors.forename: R
authors.initials: R
authors.lastname: Larionov
mesh_terms: D002986:Clinical Trials as Topic / Q000706:statistics & numerical data*; D003198:Computer Simulation / Q000706:statistics & numerical data*; D004305:Dose-Response Relationship, Drug; D006801:Humans; D016013:Likelihood Functions; D015233:Models, Statistical; D009437:Neuralgia / Q000188:drug therapy / Q000453:epidemiology
keywords: bootstrap resampling; dose-response; non-linear estimation; profile likelihood