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A Bayesian approach in design and analysis of pediatric cancer clinical trials
Author(s) -
Ye Jingjing,
Reaman Gregory,
De Claro R. Angelo,
Sridhara Rajeshwari
Publication year - 2020
Publication title -
pharmaceutical statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.421
H-Index - 38
eISSN - 1539-1612
pISSN - 1539-1604
DOI - 10.1002/pst.2039
Subject(s) - bayesian probability , clinical trial , prior probability , leverage (statistics) , medicine , drug development , bayesian inference , pediatric cancer , computer science , clinical study design , bayes' theorem , population , bayesian statistics , cancer , medical physics , machine learning , artificial intelligence , drug , pharmacology , environmental health
Summary It is well recognized that cancer drug development for children and adolescents has many challenges, from biological and societal to economic. Pediatric cancer consists of a diverse group of rare diseases, and the relatively small population of children with multiple, disparate tumor types across various age groups presents a significant challenge for drug development programs as compared to oncology drug development programs for adults. Due to the different types of cancers, limited opportunities exist for extrapolation of efficacy from adult cancer indications to children. Thus, innovative study designs including Bayesian statistical approaches should be considered. A Bayesian approach can be a flexible tool to formally leverage prior knowledge of adult or external controls in pediatric cancer trials. In this article, we provide in a case example of how Bayesian approaches can be used to design, monitor, and analyze pediatric trials. Particularly, Bayesian sequential monitoring can be useful to monitor pediatric trial results as data accumulate. In addition, designing a pediatric trial with both skeptical and enthusiastic priors with Bayesian sequential monitoring can be an efficient mechanism for early trial cessation for both efficacy and futility. The interpretation of efficacy using a Bayesian approach is based on posterior probability and is intuitive and interpretable for patients, parents and prescribers given limited data.