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A Bayesian approach to randomized controlled trials in children utilizing information from adults: the case of
Steven N Goodman1, John T Sladky
1Department of Oncology, Johns Hopkins School of Medicine and Public Health, USA.
Insights
Bayesian methods enabled a smaller trial for Guillain-Barré syndrome (GBS) treatments in children by using adult data. This approach reduces the need for extensive pediatric experimentation while ensuring treatment efficacy is rigorously assessed.
Area of Science:
- Neurology
- Biostatistics
- Pediatric Clinical Trials
Background:
- Guillain-Barré syndrome (GBS) is a rare neurological disorder causing progressive paralysis, affecting all ages with similar presentation but faster recovery in children.
- Current treatments like plasmapheresis and intravenous immune globulin (IVIg) show equal efficacy in adults, but pediatric data is limited.
- Small patient numbers in the US (100-300 annually) pose challenges for traditional pediatric clinical trials.
Purpose of the Study:
- To demonstrate the application of Bayesian methods for designing a randomized noninferiority trial in children.
- To incorporate prior treatment efficacy data from adult GBS patients into a pediatric trial design.
- To minimize the sample size and reduce pediatric experimentation.
Main Methods:
- A Bayesian normal-normal model was implemented to analyze the hazard ratio for time to independent walking.
- An evidence-based prior was constructed, equivalent to 72 children demonstrating exact treatment equivalence.
- A randomized noninferiority trial design was developed using Bayesian statistical principles.
Main Results:
- The Bayesian design yielded a required sample size of 160 children.
- Preposterior analysis confirmed a Type I error rate of 5% and 77% power.
- The constructed prior effectively leveraged adult data to inform the pediatric trial.
Conclusions:
- Bayesian approaches offer a rational method for integrating adult data into pediatric trial design, minimizing unnecessary experimentation.
- The frequentist properties of Bayesian designs can be evaluated and reported, similar to traditional methods.
- Transparent discussion of prior selection is crucial for the validity and acceptance of Bayesian clinical trials.
Background:
Guillain-Barré syndrome (GBS) is a rare neurologic disease that occurs at all ages, causing a progressive, ascending paralysis that usually resolves over weeks or months. The disease appears to be identical in children and adults, except that children recover more quickly, with fewer residua. For patients who lose the ability to walk independently, the main treatment options are plasmapheresis or intravenous immune globulin (IVIg), treatments that have shown to have identical effectiveness in adults in two large RCTs involving 388 patients. The effectiveness of the treatments in children has only been studied in small, poorly controlled studies. If one could capture all eligible patients in the United States, only about 100-300 children would be available for a trial annually.
Methods:
The goal of this case was to demonstrate how Bayesian methods could be used to incorporate prior information on treatment efficacy from adults to design a randomized noninferiority trial of IVIg versus plasmapheresis in children. A Bayesian normal-normal model on the hazard ratio of time to independent walking was implemented.
Results:
An evidence-based prior was constructed that was equivalent to 72 children showing exact equivalence between the therapies. A design was constructed based on a Bayesian normal-normal model on the hazard ratio, yielding a sample size of 160 children, with a preposterior analysis demonstrating a "Type I" error rate of 5% and a power of 77%.
Conclusions:
This case study illustrates a rational approach to constructing an evidence-based prior that would allow information from adults to formally augment data from children to minimize unnecessary pediatric experimentation. The frequentist properties of a Bayesian design can be evaluated and reported as they would be for a standard design. Discussion of the appropriate prior for such designs is both a necessary and desirable feature of Bayesian trials.
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