Imperial College London
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DrGrahamWheeler

Faculty of Medicine, School of Public Health

Honorary Senior Lecturer
 
 
 
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Contact

 

+44 (0)20 7594 8805graham.wheeler Website

 
 
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Location

 

Stadium HouseWhite City Campus

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Summary

 

Publications

Citation

BibTex format

@article{Pallmann:2020:10.1177/1740774519890146,
author = {Pallmann, P and Wan, F and Mander, AP and Wheeler, GM and Yap, C and Clive, S and Hampson, LV and Jaki, T},
doi = {10.1177/1740774519890146},
journal = {Clinical Trials},
pages = {147--156},
title = {Designing and evaluating dose-escalation studies made easy: The MoDEsT web app},
url = {http://dx.doi.org/10.1177/1740774519890146},
volume = {17},
year = {2020}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - 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 a
AU  - Pallmann,P
AU  - Wan,F
AU  - Mander,AP
AU  - Wheeler,GM
AU  - Yap,C
AU  - Clive,S
AU  - Hampson,LV
AU  - Jaki,T
DO  - 10.1177/1740774519890146
EP  - 156
PY  - 2020///
SN  - 1740-7745
SP  - 147
TI  - Designing and evaluating dose-escalation studies made easy: The MoDEsT web app
T2  - Clinical Trials
UR  - http://dx.doi.org/10.1177/1740774519890146
UR  - https://journals.sagepub.com/doi/10.1177/1740774519890146
UR  - http://hdl.handle.net/10044/1/87305
VL  - 17
ER  -
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