title: Development of a Bayesian response-adaptive trial design for the Dexamethasone for Excessive Menstruation study.
abstract: It is often unclear what specific adaptive trial design features lead to an efficient design which is also feasible to implement. This article describes the preparatory simulation study for a Bayesian response-adaptive dose-finding trial design. Dexamethasone for Excessive Menstruation aims to assess the efficacy of Dexamethasone in reducing excessive menstrual bleeding and to determine the best dose for further study. To maximise learning about the dose response, patients receive placebo or an active dose with randomisation probabilities adapting based on evidence from patients already recruited. The dose-response relationship is estimated using a flexible Bayesian Normal Dynamic Linear Model. Several competing design options were considered including: number of doses, proportion assigned to placebo, adaptation criterion, and number and timing of adaptations. We performed a fractional factorial study using SAS software to simulate virtual trial data for candidate adaptive designs under a variety of scenarios and to invoke WinBUGS for Bayesian model estimation. We analysed the simulated trial results using Normal linear models to estimate the effects of each design feature on empirical type I error and statistical power. Our readily-implemented approach using widely available statistical software identified a final design which performed robustly across a range of potential trial scenarios.
authors.affiliation: 1 MRC Tropical Epidemiology Group, London School of Hygiene & Tropical Medicine, London, UK.
authors.forename: Christian
authors.initials: C
authors.lastname: Holm Hansen
authors.affiliation: 2 Centre for Population Health Sciences, University of Edinburgh, Edinburgh, UK.
authors.forename: Pamela
authors.initials: P
authors.lastname: Warner
authors.affiliation: 3 Edinburgh Clinical Trials Unit, University of Edinburgh, Edinburgh, UK.|4 Edinburgh Health Services Research Unit, Edinburgh, UK.
authors.forename: Richard A
authors.initials: RA
authors.lastname: Parker
authors.affiliation: 5 British Heart Foundation Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, UK.
authors.forename: Brian R
authors.initials: BR
authors.lastname: Walker
authors.affiliation: 6 MRC Centre for Reproductive Health, University of Edinburgh, Edinburgh, UK.
authors.forename: Hilary Od
authors.initials: HO
authors.lastname: Critchley
authors.affiliation: 2 Centre for Population Health Sciences, University of Edinburgh, Edinburgh, UK.|4 Edinburgh Health Services Research Unit, Edinburgh, UK.
authors.forename: Christopher J
authors.initials: CJ
authors.lastname: Weir
mesh_terms: D000075522:Adaptive Clinical Trials as Topic / Q000379:methods / Q000706:statistics & numerical data*; D001499:Bayes Theorem; D056808:Biostatistics / Q000379:methods; D003198:Computer Simulation; D003907:Dexamethasone / Q000008:administration & dosage*; D004305:Dose-Response Relationship, Drug; D005260:Female; D005938:Glucocorticoids / Q000008:administration & dosage; D006801:Humans; D016014:Linear Models; D008595:Menorrhagia / Q000188:drug therapy*; D015233:Models, Statistical; D016032:Randomized Controlled Trials as Topic / Q000379:methods / Q000706:statistics & numerical data; D012984:Software
chemical_list: D005938:Glucocorticoids; D003907:Dexamethasone
keywords: Dose-finding; adaptive design; normal dynamic linear model; simulation; trial design development