title: Dunnett's many-to-one test and least square means.
abstract: Dunnett's many-to-one test is used frequently today, especially in dose-finding studies. Using Dunnett's test, the Type I error level for the comparison between the raw mean of the control and the raw means of the study drug groups can be exactly calculated for the normal data. However, this computability depends on the independence of the raw means. Unfortunately, this independence does not exist for the model-based likelihood estimates (least square means) in the cases of ANCOVA and two-way ANOVA models without interaction for unbalanced data. This paper investigates this dependence between the least square means and derives some new procedures to calculate the joint distribution of the statistic for Dunnett's test.
authors.affiliation: Biostatistics and Data Management, Aventis Pharmaceuticals Inc., Bridgewater, New Jersey 08807-0890, USA. zhenming.shun@aventis.com
authors.forename: Zhenming
authors.initials: Z
authors.lastname: Shun
authors.forename: Arthur
authors.initials: A
authors.lastname: Silverberg
authors.forename: C K
authors.initials: CK
authors.lastname: Chang
authors.forename: Peter
authors.initials: P
authors.lastname: Ouyang
mesh_terms: D000704:Analysis of Variance; D001699:Biometry / Q000379:methods; D003627:Data Interpretation, Statistical; D004305:Dose-Response Relationship, Drug; D016018:Least-Squares Analysis; D008432:Mathematical Computing; D015233:Models, Statistical; D004364:Pharmaceutical Preparations / Q000008:administration & dosage*
chemical_list: D004364:Pharmaceutical Preparations