title: A Bayesian approach to the statistical analysis of device preference studies.
abstract: Drug delivery devices are required to have excellent technical specifications to deliver drugs accurately, and in addition, the devices should provide a satisfactory experience to patients because this can have a direct effect on drug compliance. To compare patients' experience with two devices, cross-over studies with patient-reported outcomes (PRO) as response variables are often used. Because of the strength of cross-over designs, each subject can directly compare the two devices by using the PRO variables, and variables indicating preference (preferring A, preferring B, or no preference) can be easily derived. Traditionally, methods based on frequentist statistics can be used to analyze such preference data, but there are some limitations for the frequentist methods. Recently, Bayesian methods are considered an acceptable method by the US Food and Drug Administration to design and analyze device studies. In this paper, we propose a Bayesian statistical method to analyze the data from preference trials. We demonstrate that the new Bayesian estimator enjoys some optimal properties versus the frequentist estimator.
authors.affiliation: Eli Lilly and Company, Indianapolis, IN 46285, USA. FU_HAODA@LILLY.COM
authors.forename: Haoda
authors.initials: H
authors.lastname: Fu
authors.forename: Yongming
authors.initials: Y
authors.lastname: Qu
authors.forename: Baojin
authors.initials: B
authors.lastname: Zhu
authors.forename: William
authors.initials: W
authors.lastname: Huster
mesh_terms: D001499:Bayes Theorem; D018849:Controlled Clinical Trials as Topic / Q000379:methods*; D018592:Cross-Over Studies; D003627:Data Interpretation, Statistical; D016503:Drug Delivery Systems / Q000295:instrumentation*; D015195:Drug Design; D006801:Humans; D017063:Outcome Assessment, Health Care / Q000379:methods*; D057240:Patient Preference; D012107:Research Design; D014481:United States; D014486:United States Food and Drug Administration