Show simple item record

dc.contributor.authorPallmann, P
dc.contributor.authorWan, F
dc.contributor.authorMander, AP
dc.contributor.authorWheeler, GM
dc.contributor.authorYap, C
dc.contributor.authorClive, S
dc.contributor.authorHampson, LV
dc.contributor.authorJaki, T
dc.date.accessioned2019-11-18T10:03:47Z
dc.date.issued2020-04
dc.identifier.citationClinical trials (London, England), 2020, 17 (2), pp. 147 - 156
dc.identifier.issn1740-7745
dc.identifier.urihttps://repository.icr.ac.uk/handle/internal/3421
dc.identifier.eissn1740-7753
dc.identifier.doi10.1177/1740774519890146
dc.description.abstractBackground/aims Dose-escalation studies are essential in the early stages of developing novel treatments, when the aim is to find a safe dose for administration in humans. Despite their great importance, many dose-escalation studies use study designs based on heuristic algorithms with well-documented drawbacks. Bayesian decision procedures provide a design alternative that is conceptually simple and methodologically sound, but very rarely used in practice, at least in part due to their perceived statistical complexity. There are currently very few easily accessible software implementations that would facilitate their application.Methods We have created MoDEsT, a free and easy-to-use web application for designing and conducting single-agent dose-escalation studies with a binary toxicity endpoint, where the objective is to estimate the maximum tolerated dose. MoDEsT uses a well-established Bayesian decision procedure based on logistic regression. The software has a user-friendly point-and-click interface, makes changes visible in real time, and automatically generates a range of graphs, tables, and reports. It is aimed at clinicians as well as statisticians with limited expertise in model-based dose-escalation designs, and does not require any statistical programming skills to evaluate the operating characteristics of, or implement, the Bayesian dose-escalation design.Results MoDEsT comes in two parts: a 'Design' module to explore design options and simulate their operating characteristics, and a 'Conduct' module to guide the dose-finding process throughout the study. We illustrate the practical use of both modules with data from a real phase I study in terminal cancer.Conclusion Enabling both methodologists and clinicians to understand and apply model-based study designs with ease is a key factor towards their routine use in early-phase studies. We hope that MoDEsT will enable incorporation of Bayesian decision procedures for dose escalation at the earliest stage of clinical trial design, thus increasing their use in early-phase trials.
dc.formatPrint-Electronic
dc.format.extent147 - 156
dc.languageeng
dc.language.isoeng
dc.rights.urihttps://creativecommons.org/licenses/by/4.0
dc.subjectHumans
dc.subjectNeoplasms
dc.subjectQuercetin
dc.subjectAntioxidants
dc.subjectLogistic Models
dc.subjectBayes Theorem
dc.subjectMaximum Tolerated Dose
dc.subjectDose-Response Relationship, Drug
dc.subjectAlgorithms
dc.subjectResearch Design
dc.subjectSoftware
dc.subjectUser-Computer Interface
dc.subjectClinical Trials, Phase I as Topic
dc.titleDesigning and evaluating dose-escalation studies made easy: The MoDEsT web app.
dc.typeJournal Article
dcterms.dateAccepted2019-10-21
rioxxterms.versionofrecord10.1177/1740774519890146
rioxxterms.licenseref.urihttps://creativecommons.org/licenses/by/4.0
rioxxterms.licenseref.startdate2020-04
rioxxterms.typeJournal Article/Review
dc.relation.isPartOfClinical trials (London, England)
pubs.issue2
pubs.notesNot known
pubs.organisational-group/ICR
pubs.organisational-group/ICR/Primary Group
pubs.organisational-group/ICR/Primary Group/ICR Divisions
pubs.organisational-group/ICR/Primary Group/ICR Divisions/Clinical Studies
pubs.organisational-group/ICR/Primary Group/ICR Divisions/Clinical Studies/Clinical Trials & Statistics Unit
pubs.organisational-group/ICR
pubs.organisational-group/ICR/Primary Group
pubs.organisational-group/ICR/Primary Group/ICR Divisions
pubs.organisational-group/ICR/Primary Group/ICR Divisions/Clinical Studies
pubs.organisational-group/ICR/Primary Group/ICR Divisions/Clinical Studies/Clinical Trials & Statistics Unit
pubs.publication-statusPublished
pubs.volume17
pubs.embargo.termsNot known
icr.researchteamClinical Trials & Statistics Uniten_US
dc.contributor.icrauthorYap, Christinaen


Files in this item

Thumbnail

This item appears in the following collection(s)

Show simple item record

https://creativecommons.org/licenses/by/4.0
Except where otherwise noted, this item's license is described as https://creativecommons.org/licenses/by/4.0