Anna Heath1,2,3, Maryna Yaskina4, Petros Pechlivanoglou1,5, David Rios1, Martin Offringa1,5, Terry P Klassen6,7, Naveen Poonai8,9, Eleanor Pullenayegum1,2. 1. Child Health Evaluative Sciences, Peter Gilgan Centre for Research and Learning, The Hospital for Sick Children, Toronto, ON, Canada. 2. Division of Biostatistics, University of Toronto, Toronto, ON, Canada. 3. Department of Statistical Science, University College London, London, United Kingdom. 4. Women & Children's Health Research Institute, University of Alberta, Edmonton, AB, Canada. 5. Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, ON, Canada. 6. University of Manitoba, Winnipeg, MB, Canada. 7. Children's Hospital Research Institute of Manitoba, Winnipeg, MB, Canada. 8. Schulich School of Medicine and Dentistry, London, ON, Canada. 9. Children's Health Research Institute, London Health Sciences Centre, London, ON, Canada.
Abstract
BACKGROUND/AIMS: Combinations of treatments that have already received regulatory approval can offer additional benefit over Each of the treatments individually. However, trials of these combinations are lower priority than those that develop novel therapies, which can restrict funding, timelines and patient availability. This article develops a novel trial design to facilitate the evaluation of New combination therapies. This trial design combines elements of phase II and phase III trials to reduce the burden of evaluating combination therapies, while also maintaining a feasible sample size. This design was developed for a randomised trial that compares the properties of three combination doses of ketamine and dexmedetomidine, given intranasally, to ketamine delivered intravenously for children undergoing a closed reduction for a fracture or dislocation. METHODS: This trial design uses response-adaptive randomisation to evaluate different dose combinations and increase the information collected for successful novel drug combinations. The design then uses Bayesian dose-response modelling to undertake a comparative effectiveness analysis for the most successful dose combination against a relevant comparator. We used simulation methods determine the thresholds for adapting the trial and making conclusions. We also used simulations to evaluate the probability of selecting the dose combination with the highest true effectiveness the operating characteristics of the design and its Bayesian predictive power. RESULTS: With 410 participants, five interim updates of the randomisation ratio and a probability of effectiveness of 0.93, 0.88 and 0.83 for the three dose combinations, we have an 83% chance of randomising the largest number of patients to the drug with the highest probability of effectiveness. Based on this adaptive randomisation procedure, the comparative effectiveness analysis has a type I error of less than 5% and a 93% chance of correcting concluding non-inferiority, when the probability of effectiveness for the optimal combination therapy is 0.9. In this case, the trial has a greater than 77% chance of meeting its dual aims of dose-finding and comparative effectiveness. Finally, the Bayesian predictive power of the trial is over 90%. CONCLUSIONS: By simultaneously determining the optimal dose and collecting data on the relative effectiveness of an intervention, we can minimise administrative burden and recruitment time for a trial. This will minimise the time required to get effective, safe combination therapies to patients quickly. The proposed trial has high potential to meet the dual study objectives within a feasible overall sample size.
RCT Entities:
BACKGROUND/AIMS: Combinations of treatments that have already received regulatory approval can offer additional benefit over Each of the treatments individually. However, trials of these combinations are lower priority than those that develop novel therapies, which can restrict funding, timelines and patient availability. This article develops a novel trial design to facilitate the evaluation of New combination therapies. This trial design combines elements of phase II and phase III trials to reduce the burden of evaluating combination therapies, while also maintaining a feasible sample size. This design was developed for a randomised trial that compares the properties of three combination doses of ketamine and dexmedetomidine, given intranasally, to ketamine delivered intravenously for children undergoing a closed reduction for a fracture or dislocation. METHODS: This trial design uses response-adaptive randomisation to evaluate different dose combinations and increase the information collected for successful novel drug combinations. The design then uses Bayesian dose-response modelling to undertake a comparative effectiveness analysis for the most successful dose combination against a relevant comparator. We used simulation methods determine the thresholds for adapting the trial and making conclusions. We also used simulations to evaluate the probability of selecting the dose combination with the highest true effectiveness the operating characteristics of the design and its Bayesian predictive power. RESULTS: With 410 participants, five interim updates of the randomisation ratio and a probability of effectiveness of 0.93, 0.88 and 0.83 for the three dose combinations, we have an 83% chance of randomising the largest number of patients to the drug with the highest probability of effectiveness. Based on this adaptive randomisation procedure, the comparative effectiveness analysis has a type I error of less than 5% and a 93% chance of correcting concluding non-inferiority, when the probability of effectiveness for the optimal combination therapy is 0.9. In this case, the trial has a greater than 77% chance of meeting its dual aims of dose-finding and comparative effectiveness. Finally, the Bayesian predictive power of the trial is over 90%. CONCLUSIONS: By simultaneously determining the optimal dose and collecting data on the relative effectiveness of an intervention, we can minimise administrative burden and recruitment time for a trial. This will minimise the time required to get effective, safe combination therapies to patients quickly. The proposed trial has high potential to meet the dual study objectives within a feasible overall sample size.
Authors: Anna Heath; Juan David Rios; Eleanor Pullenayegum; Petros Pechlivanoglou; Martin Offringa; Maryna Yaskina; Rick Watts; Shana Rimmer; Terry P Klassen; Kamary Coriolano; Naveen Poonai Journal: Trials Date: 2021-01-06 Impact factor: 2.279
Authors: John M VanBuren; T Charles Casper; Daniel K Nishijima; Nathan Kuppermann; Roger J Lewis; J Michael Dean; Anna McGlothlin Journal: Trials Date: 2021-11-04 Impact factor: 2.279
Authors: Martin Forster; Stephen Brealey; Stephen Chick; Ada Keding; Belen Corbacho; Andres Alban; Paolo Pertile; Amar Rangan Journal: Clin Trials Date: 2021-08-18 Impact factor: 2.486