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Bayesian design using adult data to augment pediatric trials
David A Schoenfeld1, Hui Zheng, Dianne M Finkelstein
1Massachusetts General Hospital, Boston, MA, USA.
Insights
Conducting pediatric clinical trials is challenging due to low disease incidence. A Bayesian approach allows borrowing strength from adult trials, making pediatric studies more feasible and requiring fewer participants.
Area of Science:
- Biostatistics
- Clinical Trial Design
- Pediatric Research
Background:
- Pediatric clinical trials face challenges like low disease incidence and insufficient statistical power.
- Treatment efficacy data often relies on adult studies, necessitating methods to bridge this gap.
Purpose of the Study:
- To propose a Bayesian statistical approach for designing and analyzing pediatric clinical trials.
- To enable borrowing statistical strength from concurrent or prior adult trials.
Main Methods:
- Utilized a hierarchical Bayesian model where adult and pediatric trial efficacy parameters are drawn from a normal distribution.
- Incorporated expert medical opinion to guide the variance of this distribution.
- Developed methods to calculate required sample sizes and assess pediatric treatment efficacy based on adult data similarity.
Main Results:
- The Bayesian approach significantly enhances the statistical power of pediatric trials.
- This method can equivalently reduce the number of participants needed in pediatric studies.
- Leveraging adult trial data formally increases the efficiency of pediatric trial design.
Conclusions:
- The Bayesian method offers a potential solution to improve the feasibility of pediatric clinical trials.
- By borrowing strength from adult studies, smaller pediatric trials can demonstrate drug efficacy.
- A key consideration is determining the inter-study variability parameter, which may require careful clinical interpretation.
Background:
It can be difficult to conduct pediatric clinical trials because there is often a low incidence of the disease in children, making accrual slow or infeasible. In addition, low mortality and morbidity in this population make it impractical to achieve adequate power. In this case, the only evidence for treatment efficacy comes from adult trials. Since pediatric care providers are accustomed to relying on evidence from adult studies, it is natural to consider borrowing information from adult trials.
Purpose:
The goal of this article is to propose a Bayesian approach to the design and analysis of pediatric trials to allow borrowing strength from previous or simultaneous adult trials.
Methods:
We apply a hierarchical model for which the efficacy parameter from the adult trial and that of the pediatric trail are considered to be draws from a normal distribution. The choice of (the variance of) this distribution is guided by discussion with medical experts. We show that with this information, one can calculate the sample size required for the pediatric trial. We discuss how inference of these studies in pediatric populations depends on the parameter that captures the similarity of the treatment efficacy in adults compared to children.
Results:
The Bayesian approach can substantially increase the power of a pediatric clinical trial (or equivalently decrease the number of subjects required) by formally leveraging the data from the adult trial.
Limitations:
Our method relies on obtaining a value for the inter-study variability, nu, which may be difficult to describe to a clinical investigator.
Conclusions:
The Bayesian approach has the potential of making pediatric clinical trials feasible because it has the effect of borrowing strength from adult trials, thus requiring a smaller pediatric trial to show efficacy of a drug in children.
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