Bayesian Design for Pediatric Clinical Trials with Binary Endpoints When Borrowing Historical Information of Treatment E
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ORIGINAL RESEARCH
Bayesian Design for Pediatric Clinical Trials with Binary Endpoints When Borrowing Historical Information of Treatment Effect Man Jin, PhD1,2 · Qing Li1 · Amarjot Kaur1 Received: 16 April 2020 / Accepted: 8 September 2020 © The Drug Information Association, Inc 2020
Abstract The efficacy evaluation in pediatric population is an important component of drug development and is generally required by the regulatory agencies. It is often challenging to enroll pediatric subjects for a large trial especially when the incidence rate is low in certain disease areas. Bayesian framework can provide analytic avenues to effectively utilize historical information of the treatment effect and help make pediatric trials more efficient by reducing the sample size when there is evidence to suggest similarity of the treatment responses between the populations. Schoenfeld et al. (Clin Trials 6(4):297–304, 2009) proposed a Bayesian hierarchical model for efficacy extrapolation for continuous endpoints, which connects a single historical trial and the current trial by a variance parameter in the prior distribution. In this manuscript, we extend the existing model to borrow strength from multiple historical trials under the same assumptions and develop a quantitative method to borrow historical information more efficiently. Furthermore, we extend Schoenfeld’s method based on continuous endpoints to binary endpoints with a hierarchical binomial model to extrapolate efficacy. Sensitivity analyses for the underlying assumptions are discussed with simulations and the methods are illustrated with a real case study, along with some practical considerations about how to choose the prior distribution. Keywords Sample size calculations · Bayesian framework · Prior distributions · Hierarchical model · Pediatric clinical trials
Introduction In clinical trials for pediatric patients, it is often difficult to enroll patients because of low incidence rates and other inherent challenges of conducting large trials. Bayesian framework can help reduce sample size by utilizing data from historical trials if there is evidence that the treatment may have similar responses between the new study (i.e., the pediatric study) and the historical data (adult population). The assumption of the similarity of the responses can be evaluated from the evidence of the similarity of clinical rationales, pharmacokinetics, and pharmacodynamics of the medical product [1, 2]. Thus trials with Bayesian designs can alleviate the enrollment challenge, improve efficiency, * Man Jin [email protected] 1
Biostatistics and Research Decision Sciences, MRL, Merck & Co., Inc., Rahway, NJ 07065, USA
Present Address: AbbVie Inc., 1 N Waukegan Rd, North Chicago, IL 60064, USA
2
decrease the financial cost, and most importantly benefit the patients with the experimental treatment expeditiously. There are many Bayesian frameworks to incorporate results from historical trials to different populations. Dixon and Simon (1991) derived posterior distributions for subse
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