Considerations on Design and Analysis of External Control in Pediatric Oncology
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Mar 4, 2024
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Abstract
Pediatric cancer consists of a diverse group of rare diseases. Due to limited patient populations, standard randomized and controlled trials are often infeasible. As a result, single-arm trials are common in pediatric oncology and the use of external controls is often desirable or necessary to help generate actionable evidence and contextualize trial results. In this paper, we illustrate unique features in pediatric oncology clinical trials and describe their impact on the use of external controls. Various types of relevant external control data sources are described in terms of their utility and drawbacks. Statistical methodologies and design implications with external control are discussed. Two recent case studies using external controls to support pediatric oncology drug development are described in detail.
Keywords: Pediatric oncology, external controls, registry, prior clinical trial, combination therapy, Bayesian analysis
Keywords:
Pediatric oncology, external controls, registry, prior clinical trial, combination therapy, Bayesian analysis
Article Details
How to Cite
YE, Jingjing et al.
Considerations on Design and Analysis of External Control in Pediatric Oncology.
Medical Research Archives, [S.l.], v. 12, n. 1, mar. 2024.
ISSN 2375-1924.
Available at: <https://esmed.org/MRA/mra/article/view/5088>. Date accessed: 15 may 2024.
doi: https://doi.org/10.18103/mra.v12i1.5088.
Section
Research Articles
The Medical Research Archives grants authors the right to publish and reproduce the unrevised contribution in whole or in part at any time and in any form for any scholarly non-commercial purpose with the condition that all publications of the contribution include a full citation to the journal as published by the Medical Research Archives.
References
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32. Ye, J., Bi, Y., & Ting, N. (2022). How to select the initial dose for a pediatric study? Journal of Biopharmaceutical Statistics, https://doi.org/10.1080/10543406.2022.2149770.
33. Gamalo, M., Zhao, Y., Gao, A., Ye, J., Lin, J., Eshida, E., . . . Nelson, R. (2023). Extent of safety database in pediatric drug development: types of assessment, analytical precision, and pathway for extrapolation through on-target effects. Journal of Biopharmaceutical Statistics, https://doi.org/10.1080/10543406.2023.2172425.
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36. Li, H., Mukhi, V., Lu, N., & Xu, Y. (2016). A Note on Good Practice of Objective Propensity Score Design for Premarket Nonrandomized Medical Device Studies with an Example. Statistics in Biopharmaceutical Research, 8(3):DOI:10.1080/19466315.2016.1148071.
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39. Chen WC, W. C. (2020). Propensity score-integrated composite likelihood approach for augmenting the control arm of a randomized controlled trial by incorporating real-world data. J Biopharm Stat., 30(3):508-520. doi: 10.1080/10543406.2020.1730877.
40. Chandra, N., Sarkar, A., de Groot, J., Yuan, Y., & Muller, P. (2023). Bayesian Nonparametric Common Atoms Regression for Generating Synthetic Controls in Clinical Trials. JASA, https://doi.org/10.1080/01621459.2023.2231581.
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45. Yang, P., Zhao, Y., Nie, L., Vallejo, J., & Yuan, Y. (2023). SAM: Self-adapting mixture prior to dynamically borrow information from historical data in clinical trials. Biometrics, https://doi.org/10.1111/biom.13927.
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2. Cooner, F., Ye, J., & Reaman, G. (2023). Clinical trial considerations for pediatric cancer drug development. Journal of Biopharmaceutical Statistics, DOI: 10.1080/10543406.2023.2172424.
3. Renfro, L., Ji, L., Piao, J., Onar-Thomas, A., Kairalla, J., & Alonzo, T. (2019). Trial design challenges and approaches for precision oncology in rare tumors: experiences of the Children's Oncology Group (COG). JCO Precision Oncology, DOI https://doi.org/10.1200/PO.19.00060.COG), C. O. (2023). Retrieved from https://www.thecogfoundation.org/
4. Agrawal, S., Arora, S., Amiri-Kordestani, L., & et al. (2023). Use of Single-Arm Trials for US Food and Drug Administration Drug Approval in Oncology, 2002-2021. JAMA Oncol., 9(2):266-272. doi:10.1001/jamaoncol.2022.5985.
5. Chen, J., Li, X., Lu, C., Yuan, S., Yung, G., Ye, J., . . . Lin, J. (2023). Considerations for Master Protocols Using External Controls. Journal of Biopharmaceutical Statistics.
6. Mishra-Kalyani, P. S. (2022). External control arms in oncology: current use and future directions. Annals of Oncology , https://doi.org/10.1016/j.annonc.2021.12.015.
7. Adamczewska-Wawrzynowicz, K., Wiacek, A., & et al. (2023). Modern treatment strategies in pediatric oncology and hematology. Discover Oncology, https://doi.org/10.1007/s12672-023-00658-7.
8. DuBois, S., Krailo, M., Gebhardt, M., & et al. (2015). Comparative evaluation of local control strategies in localized Ewing sarcoma of bone: a report from the Children's Oncology Group. Cancer, 121(3):467-75. doi: 10.1002/cncr.29065.
9. Adams, H., Adams, H., Jackson, C., Rincon-Torroella, J., Jallo, G., & Quinones-Hinojosa, A. (2016). Evaluating extent of resection in pediatric glioblastoma: a multiple propensity score-adjusted population-based analysis. Childs Nerv Syst, 32(3):493-503. doi: 10.1007/s00381-015-3006-x.
10. (COG), C. O. (2023). Retrieved from https://www.thecogfoundation.org/
11. Unger, J., & Fleury, M. (2021). Nationally representative estimates of the participation of cancer patients in clinical research studies according to the commission on cancer. Journal of Clinical Oncology, DOI: 10.1200/JCO.2020.39.28_suppl.74 .
12. Ward, E., DeSantis, C., Robbins, A., & et al. (2014). Childhood and Adolescent Cancer Statistics, 2014. CA Cancer J Clin, 64:83-103.
13. (NCI), N. C. (n.d.). Statistics for cancers in children, adolescents, and young adults. Retrieved from https://nccrexplorer.ccdi.cancer.gov/
14. Ager, B., Christensen, M., Burt, L., & Poppe, M. (2019). The value of high-dose radiotherapy in intracranial ependymoma. Pediatr Blood Cancer, 66(6):e27697. doi: 10.1002/pbc.27697.
15. Bilimoria, K., Stewart, A., Winchester, D., & Ko, C. (2008). The National Cancer Data Base: A Powerful Initiative to Improve Cancer Care in the United States. Ann Surg Oncol, 15(3): 683–690. doi: 10.1245/s10434-007-9747-3.
16. (FDA), U. F. (2018, December). Framework for FDA's Real-world evidence program. Retrieved from https://www.fda.gov/media/120060/download
17. Ma, X., Liu, Y., Liu, Y., & et al. (2018). Pan-cancer genome and transcriptome analyses of 1,699 pediatric leukaemias and solid tumors. Nature, 555:371-6.
18. Grobner, S., Worst, B., Weischendieldt, J., & et al. (2018). The landscape of genomic alterations across childhood cancers. Nature, 555:321-7 (FDA), U. F. (2019, November). Adaptive Design Clinical Trials for Drugs and Biologics Guidance for Industry. Retrieved from https://www.fda.gov/media/78495/download
19. Barrett, J., Bishai, R., & et al. (2018). Challenges and opportunities in the development of medical therapies for pediatric populations and the role of extrapolation. Clin Pharmacol Ther., 103(3):376–378.
20. Mohamad, S., Menon, R., Gibbs, J., & et al. (2019). Accelerating Drug Development in Pediatric Oncology With the Clinical Pharmacology Storehouse. The Journal of Clinical Pharmacology, 59(5):625-637.
21. (FDA), U. F. (2019, March). Considerations for the Inclusion of Adolescent Patients in Adult Oncology Clinical Trials. Retrieved from https://www.fda.gov/media/113499/download
22. Gaspar, N., Marshall, L., Binner, D., & et al. (2018). Joint adolescent adult early phase clinical trials to improve access to new drugs for adolescents with cancer proposals from the multistakeholder platform—ACCELERATE. Ann Oncol, 29(3):766-771.
23. Moreno, L., Pearson, A., Paeletti, X., & et al. (2017). Early phase clinical trials of anticancer agents in children and adolescents—an ITCC perspective. Nat Rev Clin Oncol., 14(8):497-507.
24. Parsons, H., Harlan, L., Seibel, N., Stevens, J., & Keegan, T. (2011). Clinical Trial Pariticipation and Time to Treatment Among Adolescents and Young Adults with Cancer: Does Age at Diagnosis or Insurance Make a Difference? Journal of Clinical Oncology, 29:4045-4053.DOI: 10.1200/JCO.2011.36.2954.
25. Pole, J., Barber, R., Bergeron, R., & et al. (2017). Most children with cancer are not enrolled on a clinical trial in Canada: a population-based study. BMC Cancer, 17, 402. https://doi.org/10.1186/s12885-017-3390-6.
26. Brown, P. (2013). Treatment of infant leukemias: challenge and promise. Hematology Am Soc Hematol Educ Program, 596–600. doi:10.1182/asheducation-2013.1.596.
27. Gamis, A., Alonzo, T., & et al. (2014). Gemtuzumab ozogamicin in children and adolescents with de novo acute myeloid leukemia improves event-free survival by reducing relapse risk: results from the randomized phase III Children’s Oncology Group trial AAML0531. Journal of Clinical Oncology, 32(27):3021-32. doi: 10.1200/JCO.2014.55.3628.
28. Pocock, S. (1976). The combination of randomized and historical controls in clinical trials. J Chronic Dis, 29(3):175-88. doi: 10.1016/0021-9681(76)90044-8.
29. Council, T. C. (2023). The C17 Council. Retrieved from https://www.c17.ca/
30. Europe, I. T. (2023). INNOVATIVE THERAPIES FOR CHILDREN WITH CANCER (ITCC). Retrieved from https://www.itcc-consortium.org/
31. Akalu, A., Meng, X., Reaman, G., Ma, L., Yuan, W., & Ye, J. (2021). A review of the experience with pediatricwritten requests issued for oncology drug products. Pediatric Bloood & Cancer, Feb;68(2):e28828. doi: 10.1002/pbc.28828.
32. Ye, J., Bi, Y., & Ting, N. (2022). How to select the initial dose for a pediatric study? Journal of Biopharmaceutical Statistics, https://doi.org/10.1080/10543406.2022.2149770.
33. Gamalo, M., Zhao, Y., Gao, A., Ye, J., Lin, J., Eshida, E., . . . Nelson, R. (2023). Extent of safety database in pediatric drug development: types of assessment, analytical precision, and pathway for extrapolation through on-target effects. Journal of Biopharmaceutical Statistics, https://doi.org/10.1080/10543406.2023.2172425.
34. Choi, Y., Barbier, N., Furst-Recktenwald, S., Ye, J., Wajsbrot, D., Ishida, E., & Gamalo, M. (2022). Analysis and reporting of pediatric growth and development assessment from clinical trials: overview and challenges. Journal of Biopharmaceutical Statistics , https://doi.org/10.1080/10543406.2022.2154357.
35. Yue LQ, L. N. (2014). Designing premarket observational comparative studies using existing data as controls: challenges and opportunities. J Biopharm Stat., 24(5):994-1010. doi: 10.1080/10543406.2014.926367.
36. Li, H., Mukhi, V., Lu, N., & Xu, Y. (2016). A Note on Good Practice of Objective Propensity Score Design for Premarket Nonrandomized Medical Device Studies with an Example. Statistics in Biopharmaceutical Research, 8(3):DOI:10.1080/19466315.2016.1148071.
37. Stuart, E. a. (2007). Best Practices in Quasi-Experimental Designs: Matching methods for causal inference. Chapter 11. In J. O. (Ed.)., Best Practices in Quantitative Social Science (pp. 155-176). Thousand Oaks, CA: Sage Publications.
38 Wang C, L. N. (2020). Propensity score-integrated composite likelihood approach for incorporating real-world evidence in single-arm clinical studies. J Biopharm Stat. , 30(3):495-507. doi: 10.1080/10543406.2019.1684309.
39. Chen WC, W. C. (2020). Propensity score-integrated composite likelihood approach for augmenting the control arm of a randomized controlled trial by incorporating real-world data. J Biopharm Stat., 30(3):508-520. doi: 10.1080/10543406.2020.1730877.
40. Chandra, N., Sarkar, A., de Groot, J., Yuan, Y., & Muller, P. (2023). Bayesian Nonparametric Common Atoms Regression for Generating Synthetic Controls in Clinical Trials. JASA, https://doi.org/10.1080/01621459.2023.2231581.
41. Schmidli, H. G. (2014). Robust meta-analytic-predictive priors in clinical trials with historical control information. Biometrics, 70(4): 1023-1032. https://doi.org/10.1111/biom.12242 .
42. Hobbs B, C. B. (2011). Hierarchical commensurate and power prior models for adaptive incorporation of historical information in clinical trials. . Biometrics, 67(3):1047-56.
43. Chen, M., & Ibrahim, J. (2000). Power prior distributions for regression models. Statist. Sci., 15(1): 46-60 (1 February 2000). DOI: 10.1214/ss/1009212673.
44. Chen, M., & Ibrahim, J. (2006). The Relationship Between the Power Prior and Hierarchical Model. Bayesian Analysis, 1(3):551-574.
45. Yang, P., Zhao, Y., Nie, L., Vallejo, J., & Yuan, Y. (2023). SAM: Self-adapting mixture prior to dynamically borrow information from historical data in clinical trials. Biometrics, https://doi.org/10.1111/biom.13927.
46. Zhou, T., & Ji, Y. (2021). Incorporating external data into the analysis of clinical trials via Bayesian additive regression trees. Statistics in Medicine, 40(28): 6421-6442. https://doi.org/10.1002/sim.9191.
47. (FDA), U. F. (2020, December). Interacting with the FDA on Complex Innovative Trial Designs for Drugs and Biological Products. Retrieved from https://www.fda.gov/media/130897/download?attachment
48. (FDA), U. F. (n.d.). FDA Briefing Document, BLA 761176 I-omburtamab by Y-mAbs Therapeutics. Retrieved from https://www.fda.gov/media/162614/download
49. Amgen. (2018, March 07). Background Materials for the Oncology Drugs Advisory Committee Meeting (ODAC) on Blinatumomab. Retrieved from https://www.fda.gov/media/111641/download
50. Sridhara, R., Marchenko, O., Jiang, Q., & et al. (2023). Statistical Considerations in Pediatric Cancer Trials: Report of American Staistical Association Biopharmaceutical Section Open Forum Discussions. Statistics in Biopharmaceutical Research, https://doi.org/10.1080/19466315.2023.2238650.