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

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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.

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Acknowledgements

The authors thank two referees for their insightful comments to improve the manuscript.

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Authors and Affiliations

  1. Global Statistics and Data Sciences (GSDS), BeiGene (USA), Fulton, MD, USA

    Jingjing Ye

  2. Oncology Center of Excellence (OCE) and Center for Drug Evaluation and research (CDER), U.S. Food and Drug Administration (FDA), Silver Spring, MD, USA

    Gregory Reaman

Authors
  1. Jingjing Ye
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  2. Gregory Reaman
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Contributions

JY and GR designed the comparison. JY developed the method and did the simulations. JY and GR drafted and approved the manuscript.

Corresponding author

Correspondence to Jingjing Ye.

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Conflict of interest

No conflicts of interest from the authors. JY is an employee of BeiGene.

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Ye, J., Reaman, G. Improving Early Futility Determination by Learning from External Data in Pediatric Cancer Clinical Trials. Stat Biosci 14, 337–351 (2022). https://doi.org/10.1007/s12561-021-09332-4

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  • DOI: https://doi.org/10.1007/s12561-021-09332-4

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