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Improving Early Futility Determination by Learning from External Data in Pediatric Cancer Clinical Trials

Author

Listed:
  • Jingjing Ye

    (Global Statistics and Data Sciences (GSDS), BeiGene (USA))

  • Gregory Reaman

    (U.S. Food and Drug Administration (FDA))

Abstract

Pediatric cancer consists of a diverse group of rare diseases. 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. A recent review paper searched the written requests that were issued by the US FDA between 2001 and 2019. Many of the completed pediatric trials over the past 19 years have led to conclusions that the cancer drugs developed for adult cancer indications have not demonstrated sufficient effectiveness within the context of limited phase 1 and/or phase 2 studies in heavily pretreated patients (Akalu et al. in Pediatr Blood Cancer. https://doi.org/10.1002/pbc.28828 , 2020). Faster learning and the implementation of futility criteria in the trial design should be considered in pediatric trials when the potential beneficial effects of investigational drugs may be unclear. In this paper, the authors compare the commonly used Simon’s 2-stage design in pediatric cancer trials to Bayesian sequential monitoring. The results show that the chance to stop for futility is at least doubled when a Bayesian design is used when compared to Simon’s 2-stage. The lower the true response rates are, the greater the number of patients would be saved from exposure to an ineffective treatment. To overcome the limitation of a small population and limited extrapolation opportunities, the innovative approach using Bayesian strategy to allow leveraging adult or external data in pediatric cancer trials should be considered.

Suggested Citation

  • Jingjing Ye & Gregory Reaman, 2022. "Improving Early Futility Determination by Learning from External Data in Pediatric Cancer Clinical Trials," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 14(2), pages 337-351, July.
    • Handle: RePEc:spr:stabio:v:14:y:2022:i:2:d:10.1007_s12561-021-09332-4
    DOI: 10.1007/s12561-021-09332-4
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    References listed on IDEAS

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