title: Dose-response curve estimation: a semiparametric mixture approach.
abstract: In the estimation of a dose-response curve, parametric models are straightforward and efficient but subject to model misspecifications; nonparametric methods are robust but less efficient. As a compromise, we propose a semiparametric approach that combines the advantages of parametric and nonparametric curve estimates. In a mixture form, our estimator takes a weighted average of the parametric and nonparametric curve estimates, in which a higher weight is assigned to the estimate with a better model fit. When the parametric model assumption holds, the semiparametric curve estimate converges to the parametric estimate and thus achieves high efficiency; when the parametric model is misspecified, the semiparametric estimate converges to the nonparametric estimate and remains consistent. We also consider an adaptive weighting scheme to allow the weight to vary according to the local fit of the models. We conduct extensive simulation studies to investigate the performance of the proposed methods and illustrate them with two real examples.
authors.affiliation: Department of Biostatistics, The University of Texas M.D. Anderson Cancer Center, Houston, Texas 77030, USA. yyuan@mdanderson.org
authors.forename: Ying
authors.initials: Y
authors.lastname: Yuan
authors.forename: Guosheng
authors.initials: G
authors.lastname: Yin
mesh_terms: D000818:Animals; D001699:Biometry / Q000379:methods*; D003198:Computer Simulation; D004305:Dose-Response Relationship, Drug; D004360:Drug Therapy, Computer-Assisted / Q000379:methods*; D006801:Humans; D008954:Models, Biological; D015233:Models, Statistical; D017063:Outcome Assessment, Health Care / Q000379:methods*; D018675:Toxicity Tests / Q000379:methods*