title: A new classification approach for comparing two active treatments when there is no prior projection on which one is better.
abstract: We developed a new classification approach in this paper to compare two active treatments. This approach is especially useful when there is no prior judgment on which treatment is better and the traditional hypothesis testing approach is thus not applicable. Our method classifies all the possible outcomes into categories and draws conclusions on the difference in the outcome measurement between two treatment arms according to the location of the confidence interval for the treatment difference in the response variable. This method controls the misclassification rate regardless of the true difference in the response between the two treatment arms. The method was applied to a diabetes clinical trial.
authors.affiliation: Department of Global Statistical Science, Eli Lilly and Company, Indianapolis, IN 46285, USA. quyo@lilly.com
authors.forename: Yongming
authors.initials: Y
authors.lastname: Qu
authors.forename: Rong
authors.initials: R
authors.lastname: Liu
authors.forename: Alexei
authors.initials: A
authors.lastname: Dmitrienko
authors.forename: Walter
authors.initials: W
authors.lastname: Offen
mesh_terms: D000465:Algorithms; D056808:Biostatistics; D016001:Confidence Intervals; D003920:Diabetes Mellitus / Q000097:blood / Q000188:drug therapy; D006442:Glycated Hemoglobin / Q000378:metabolism; D006801:Humans; D061268:Insulin Lispro / Q000008:administration & dosage; D017063:Outcome Assessment, Health Care / Q000706:statistics & numerical data; D016032:Randomized Controlled Trials as Topic / Q000145:classification / Q000706:statistics & numerical data*; D013812:Therapeutics / Q000145:classification; D016896:Treatment Outcome
chemical_list: D006442:Glycated Hemoglobin; D061268:Insulin Lispro; C517652:hemoglobin A1c protein, human