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Designing and evaluating dose-escalation studies made easy: the MoDEsT web app

Wheeler, G; Pallmann, P; Wan, F; Mander, A; Yap, C; Clive, S; Hampson, L; (2019) Designing and evaluating dose-escalation studies made easy: the MoDEsT web app. Clinical Trials 10.1177/1740774519890146. (In press). Green open access

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Abstract

Background/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.

Type: Article
Title: Designing and evaluating dose-escalation studies made easy: the MoDEsT web app
Open access status: An open access version is available from UCL Discovery
DOI: 10.1177/1740774519890146
Publisher version: https://doi.org/10.1177/1740774519890146
Language: English
Additional information: This article is distributed under the terms of the Creative Commons Attribution 4.0 License (https://www.creativecommons.org/licenses/by/4.0/) which permits any use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/open-access-at-sage).
Keywords: Phase I clinical trial, dose-finding study, logistic model, Bayesian statistics, graphical user interface, shiny app, R software
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences > Cancer Institute
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences > Cancer Institute > CRUK Cancer Trials Centre
URI: https://discovery.ucl.ac.uk/id/eprint/10085430
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