title: An adaptive design for updating the threshold value of a continuous biomarker.
abstract: Potential predictive biomarkers are often measured on a continuous scale, but in practice, a threshold value to divide the patient population into biomarker 'positive' and 'negative' is desirable. Early phase clinical trials are increasingly using biomarkers for patient selection, but at this stage, it is likely that little will be known about the relationship between the biomarker and the treatment outcome. We describe a single-arm trial design with adaptive enrichment, which can increase power to demonstrate efficacy within a patient subpopulation, the parameters of which are also estimated. Our design enables us to learn about the biomarker and optimally adjust the threshold during the study, using a combination of generalised linear modelling and Bayesian prediction. At the final analysis, a binomial exact test is carried out, allowing the hypothesis that 'no population subset exists in which the novel treatment has a desirable response rate' to be tested. Through extensive simulations, we are able to show increased power over fixed threshold methods in many situations without increasing the type-I error rate. We also show that estimates of the threshold, which defines the population subset, are unbiased and often more precise than those from fixed threshold studies. We provide an example of the method applied (retrospectively) to publically available data from a study of the use of tamoxifen after mastectomy by the German Breast Study Group, where progesterone receptor is the biomarker of interest. © 2016 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.
authors.affiliation: AstraZeneca, Global Medicines Development, Biometrics and Information Sciences, Mereside, Alderley Park, Macclesfield, SK10 4TG, U.K.. amy.v.spencer86@gmail.com.
authors.forename: Amy V
authors.initials: AV
authors.lastname: Spencer
authors.affiliation: Roche Pharmaceuticals, 6 Falcon Way, Welwyn Garden City, AL7 1TW, U.K.
authors.forename: Chris
authors.initials: C
authors.lastname: Harbron
authors.affiliation: MRC Biostatistics Unit Hub for Trials Methodology Research, Cambridge Institute of Public Health, Cambridge Biomedical Campus, Forvie Site, Robinson Way, Cambridge, CB2 0SR, U.K.
authors.forename: Adrian
authors.identifier: 0000-0002-0742-9040
authors.initials: A
authors.lastname: Mander
authors.affiliation: MRC Biostatistics Unit Hub for Trials Methodology Research, Cambridge Institute of Public Health, Cambridge Biomedical Campus, Forvie Site, Robinson Way, Cambridge, CB2 0SR, U.K.
authors.forename: James
authors.initials: J
authors.lastname: Wason
authors.affiliation: AstraZeneca, Global Medicines Development, Biometrics and Information Sciences, Mereside, Alderley Park, Macclesfield, SK10 4TG, U.K.
authors.forename: Ian
authors.initials: I
authors.lastname: Peers
mesh_terms: D001499:Bayes Theorem; D015415:Biomarkers; D002986:Clinical Trials as Topic; D006801:Humans; D018579:Patient Selection; D012107:Research Design
chemical_list: D015415:Biomarkers
keywords: Bayesian prediction; adaptive design; biomarker-guided trial; enrichment; threshold determination