Michael J Grayling1, James Ms Wason2,3. 1. Population Health Sciences Institute, Newcastle, NE2 4AX, UK. michael.grayling@newcastle.ac.uk. 2. Population Health Sciences Institute, Newcastle, NE2 4AX, UK. 3. MRC Biostatistics Unit, University of Cambridge, Cambridge, CB2 0SR, UK.
Abstract
BACKGROUND: Multi-arm designs provide an effective means of evaluating several treatments within the same clinical trial. Given the large number of treatments now available for testing in many disease areas, it has been argued that their utilisation should increase. However, for any given clinical trial there are numerous possible multi-arm designs that could be used, and choosing between them can be a difficult task. This task is complicated further by a lack of available easy-to-use software for designing multi-arm trials. RESULTS: To aid the wider implementation of multi-arm clinical trial designs, we have developed a web application for sample size calculation when using a variety of popular multiple comparison corrections. Furthermore, the application supports sample size calculation to control several varieties of power, as well as the determination of optimised arm-wise allocation ratios. It is built using the Shiny package in the R programming language, is free to access on any device with an internet browser, and requires no programming knowledge to use. It incorporates a variety of features to make it easier to use, including help boxes and warning messages. Using design parameters motivated by a recently completed phase II oncology trial, we demonstrate that the application can effectively determine and evaluate complex multi-arm trial designs. CONCLUSIONS: The application provides the core information required by statisticians and clinicians to review the operating characteristics of a chosen multi-arm clinical trial design. The range of designs supported by the application is broader than other currently available software solutions. Its primary limitation, particularly from a regulatory agency point of view, is its lack of validation. However, we present an approach to efficiently confirming its results via simulation.
BACKGROUND: Multi-arm designs provide an effective means of evaluating several treatments within the same clinical trial. Given the large number of treatments now available for testing in many disease areas, it has been argued that their utilisation should increase. However, for any given clinical trial there are numerous possible multi-arm designs that could be used, and choosing between them can be a difficult task. This task is complicated further by a lack of available easy-to-use software for designing multi-arm trials. RESULTS: To aid the wider implementation of multi-arm clinical trial designs, we have developed a web application for sample size calculation when using a variety of popular multiple comparison corrections. Furthermore, the application supports sample size calculation to control several varieties of power, as well as the determination of optimised arm-wise allocation ratios. It is built using the Shiny package in the R programming language, is free to access on any device with an internet browser, and requires no programming knowledge to use. It incorporates a variety of features to make it easier to use, including help boxes and warning messages. Using design parameters motivated by a recently completed phase II oncology trial, we demonstrate that the application can effectively determine and evaluate complex multi-arm trial designs. CONCLUSIONS: The application provides the core information required by statisticians and clinicians to review the operating characteristics of a chosen multi-arm clinical trial design. The range of designs supported by the application is broader than other currently available software solutions. Its primary limitation, particularly from a regulatory agency point of view, is its lack of validation. However, we present an approach to efficiently confirming its results via simulation.
Authors: Elias Laurin Meyer; Peter Mesenbrink; Tobias Mielke; Tom Parke; Daniel Evans; Franz König Journal: Trials Date: 2021-03-04 Impact factor: 2.279
Authors: David A Jolliffe; Hayley Holt; Matthew Greenig; Mohammad Talaei; Natalia Perdek; Paul Pfeffer; Giulia Vivaldi; Sheena Maltby; Jane Symons; Nicola L Barlow; Alexa Normandale; Rajvinder Garcha; Alex G Richter; Sian E Faustini; Christopher Orton; David Ford; Ronan A Lyons; Gwyneth A Davies; Frank Kee; Christopher J Griffiths; John Norrie; Aziz Sheikh; Seif O Shaheen; Clare Relton; Adrian R Martineau Journal: BMJ Date: 2022-09-07