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Statistical Considerations in the Development of Companion Diagnostic Device

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Statistical Methods in Biomarker and Early Clinical Development
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Abstract

Since the concept of companion diagnostics was introduced in 1990s, personalized medicine has become more and more prominent. A well-established example of personalized medicine is the approval of the drug Herceptin in patients with breast cancer who tested positive for human epidermal growth factor receptor 2 (HER2). This is also one of the earliest examples of a co-development drug and companion diagnostic (hereafter CDx) model before there was a formal regulatory process in place. The device (or test) technology, cutoff points, and performance of a CDx can all vary, and thus different tests are likely to identify different populations for a given drug. These aspects are critical components to clinical trial design for CDx corresponding therapeutic product.

Drug companies like to have a predictable regulatory route of drug development when companion diagnostic is involved. It is also important for drug companies to understand specific sets of statistical and clinical trial design questions to be addressed. In this chapter, we will discuss statistical considerations pertaining to companion diagnostics. In the first section, we will provide the overview of personalized medicine from statistical perspective. The second section discusses statistical issues relative to CDx development such as primary endpoint and data analysis need to support CDx clinical validation. In the third section, we will discuss statistical considerations including cutoff/threshold determination for CDx, Phase II/III clinical trial study designs; CDx bridging studies, and pre-screening bias. In the last two sections, we will present conclusion and recommendations. We hope that the book chapter will provide a good reference for statisticians, clinicians, and researchers from industry for statistical issues pertaining to companion diagnostic device in personalized medicine.

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References

  • Simon, R.M., Genomic Clinical Trials and Predictive Medicine. 2013: Cambridge University Press.

    Google Scholar 

  • LaThangue, N.B., and Kerr, D.J. (2011).Predictive biomarkers :a paradigm shift towards personalized cancer medicine. Nat. Rev. Clin. Oncol. 8, 587–596.doi: https://doi.org/10.1038/nrclinonc.2011.121

    Article  Google Scholar 

  • The US Food and Drug Administration: In Vitro Companion Diagnostic Devices Guidance for Industry and Food and Drug Administration Staff 2014

    Google Scholar 

  • Jakka S, Rossbach M. An economic perspective on personalized medicine. The HUGO J 2013; 7:1.

    Article  Google Scholar 

  • Davies B. M., Rikabi S., French A., Pinedo-Villanueva R., Morrey M. E., Wartolowska K., et al. (2014). Quantitative assessment of barriers to the clinical development and adoption of cellular therapies: a pilot study. J. Tissue Eng

    Google Scholar 

  • List of Cleared or Approved Companion Diagnostic Devices (In Vitro and Imaging Tools) http://www.fda.gov/companiondiagnostics.

  • Li M (2015) Statistical consideration and challenges in bridging study of personalized medicine. J Biopharm Stat. 25(3):397–407. doi: https://doi.org/10.1080/10543406.2014.920340

    Article  Google Scholar 

  • CLSI. Evaluation of Detection Capability for Clinical Laboratory Measurement Procedures; Approved Guideline—Second Edition. CLSI document EP17-A2. Wayne, PA: Clinical and Laboratory Standards Institute, 2012.

    Google Scholar 

  • European Medicines Agency. Reflection paper on co-development of pharmacogenomic biomarkers and assays in the context of drug development. European Medicines Agency [online], http://www.ema.europa.eu/docs/en_GB/document_library/Scientific_guideline/2010/07/WC500094445.pdf (2010).

  • Moore, M. W., Babu, D. & Cotter, P. D. Challenges in the codevelopment of companion diagnostics. Per. Med. 9, 485–496 (2012).

    Article  Google Scholar 

  • Fridlyand J, Simon RM, Walrath JC, et al. Considerations for the successful co-development of targeted cancer therapies and companion diagnostics. Nat Rev Drug Discov. 2013;12(10):743–755. doi: https://doi.org/10.1038/nrd4101

    Article  Google Scholar 

  • Yip, V., Hawcutt, D., and Pirmohamed, M. (2015). Pharmacogenetic markers of drug efficacy and toxicity. Clin. Pharmacol. Ther. 98, 61–70.doi: https://doi.org/10.1002/cpt.135

    Article  Google Scholar 

  • Trusheim, M.R., Burgess, B., Hu, S. X., Long, T., Averbuch, S.D., Flynn, A.A., et al.(2011).Quantifying factors for the success of stratified medicine. Nat. Rev. Drug Discov. 10, 817–833. doi:https://doi.org/10.1038/nrd3557

    Article  Google Scholar 

  • Alosh, M. and M.F. Huque, A flexible strategy for testing subgroups and overall population. Statistics in Medicine, 2009. 28(1): p. 3–23.

    Article  MathSciNet  Google Scholar 

  • Wang, S.-J., R.T. O’Neill, and H.M.J. Hung, Approaches to evaluation of treatment effect in randomized clinical trials with genomic subset. Pharmaceutical Statistics, 2007. 6(3): p. 227–244.

    Article  Google Scholar 

  • Kim, E.S., et al., The BATTLE Trial: Personalizing Therapy for Lung Cancer. Cancer Discovery, 2011.

    Google Scholar 

  • Liu, A., et al., A threshold sample-enrichment approach in a clinical trial with heterogeneous subpopulations. Clinical trials (London, England), 2010. 7(5): p. 537–545.

    Article  Google Scholar 

  • Rosenblum, M. and M.J. van der Laan, Optimizing randomized trial designs to distinguish which subpopulations benefit from treatment. Biometrika, 2011. 98(4): p. 845–860.

    Article  MathSciNet  Google Scholar 

  • Simon, N. and R. Simon, Adaptive enrichment designs for clinical trials. Biostatistics (Oxford, England), 2013. 14(4): p. 613–625

    Article  Google Scholar 

  • Tang R., Biomarker-Defined Subgroup Selection Adaptive Design for Phase III Confirmatory Trial with Time-to-Event Data: Comparing Group Sequential and Various Adaptive Enrichment Designs 2017 Statistics in Biosciences DOI: https://doi.org/10.1007/s12561-017-9198-8

    Article  Google Scholar 

  • Zhiwei Zhang, Meijuan Li, Min Lin, Guoxing Soon, Tom Greene, Changyu Shen, Subgroup selection in adaptive signature designs of confirmatory clinical trials. Royal Statistical Society- Applied Statistics, 2017, 66:345–361

    Article  MathSciNet  Google Scholar 

  • Buyse, M.; Michiels, S.; Sargent, D.J.; Grothey, A.; Matheson, A.; de Gramont, A. Integrating biomarkers in clinical trials. Expert Rev. Mol. Diagn. 2011, 11(2), 171–182.

    Article  Google Scholar 

  • Freidlin, B.; McShane, L.M.; Korn, E.L. Randomized Clinical Trials With Biomarkers: Design Issues. J. Natl. Cancer Inst. 2010, 102:152–160.

    Article  Google Scholar 

  • FDA Guidance Design Considerations for Pivotal Clinical Investigations for Medical Devices - Guidance for Industry, Clinical Investigators, Institutional Review Boards and Food and Drug Administration Staff 2013

    Google Scholar 

  • Li, M., Pennello, G., Wu, J., et al. Personalized Medicine. Encyclopedia of Biopharmaceutical Statistics, 2016.

    Google Scholar 

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Acknowledgements

The authors gratefully thank Drs. Thomas Gross, Tinghui Yu, Jincao Wu, and Ram Tiwari from Center for Devices and Radiological Health, U.S. Food and Drug Administration.

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Li, M., Tang, R. (2019). Statistical Considerations in the Development of Companion Diagnostic Device. In: Fang, L., Su, C. (eds) Statistical Methods in Biomarker and Early Clinical Development. Springer, Cham. https://doi.org/10.1007/978-3-030-31503-0_5

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