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A Bayesian response-adaptive dose-finding and comparative effectiveness trial
Anna Heath1,2,3, Maryna Yaskina4, Petros Pechlivanoglou1,5
1Child Health Evaluative Sciences, Peter Gilgan Centre for Research and Learning, The Hospital for Sick Children, Toronto, ON, Canada.
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.
Insights
This study introduces a novel trial design for evaluating new combination therapies, like intranasal ketamine and dexmedetomidine, in children. The adaptive design efficiently identifies optimal doses and compares effectiveness, speeding up access to safe treatments.
Area of Science:
- Clinical Trials
- Pharmacology
- Pediatric Anesthesiology
Background:
- Combination therapies offer benefits over individual treatments but face trial funding and prioritization challenges.
- Novel trial designs are needed to efficiently evaluate combination therapies, especially those using approved drugs.
- This study focuses on evaluating intranasal ketamine and dexmedetomidine combinations for pediatric fracture reduction.
Purpose of the Study:
- To develop and validate a novel, adaptive trial design for evaluating combination therapies.
- To facilitate the assessment of multiple dose combinations of ketamine and dexmedetomidine.
- To compare the effectiveness of the optimal combination against a standard treatment in pediatric patients.
Main Methods:
- Utilizing response-adaptive randomization to allocate patients to different dose combinations.
- Employing Bayesian dose-response modeling for comparative effectiveness analysis.
- Conducting simulation studies to determine adaptive thresholds and evaluate design operating characteristics.
Main Results:
- The adaptive design demonstrated an 83% chance of allocating the most patients to the most effective dose combination.
- Simulations showed a type I error <5% and 93% power for non-inferiority conclusion when the optimal combination is highly effective.
- The trial design achieved >77% probability of meeting dual objectives (dose-finding and comparative effectiveness) with >90% Bayesian predictive power.
Conclusions:
- The novel trial design efficiently determines optimal drug doses and assesses comparative effectiveness simultaneously.
- This approach minimizes administrative burden and recruitment time, accelerating patient access to effective combination therapies.
- The proposed design offers a feasible and powerful strategy for evaluating new combination therapies within a manageable sample size.
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