Advertisement

Challenges of Bridging Studies in Biomarker Driven Clinical Trials: The Impact of Companion Diagnostic Device Performance on Clinical Efficacy

  • Szu-Yu TangEmail author
  • Bonnie LaFleur
Conference paper
Part of the Springer Proceedings in Mathematics & Statistics book series (PROMS, volume 218)

Abstract

Personalized medicine involves the co-development of both the therapeutic agent (Rx) and a companion diagnostic device (CDx), which directs a group of patients to a particular treatment. There are instances, however, when there are competing, or multiple CDx products for a given Rx. Drivers for multiple CDx products can be driven by improved efficiency, cost, novel technologies, or updated techniques over time. In these instances, concordance between the old assay (e.g., the assay used in the clinical trial or comparator companion diagnostic device in this paper) and a new assay (follow-on companion diagnostic device) needs to be assessed. Discrepancies between the old and new assays, and specifically the impact of discordance on clinical efficacy, need to be evaluated. Studies that establish similarity between two or more CDx products are called bridging studies. We provide a statistical framework for method comparison studies where there is bias in measurement of one or both assessments. We then present a simulation study to evaluate the statistical impact of an imperfect CDx on the sensitivity and specificity of the follow-on companion diagnostic device. Further, we demonstrate the influence of the CDx accuracy on clinical efficacy in the context of an enrichment clinical trial.

Keywords

Bridging studies Companion diagnostic device (CDx) Comparator companion diagnostic device Follow-on companion diagnostic device Personalized medicine 

Notes

Acknowledgements

The authors gratefully thank Dr. Chang Xu from Qiagen and Dominic LaRoche from HTG Molecular Diagnostics.

References

  1. 1.
    Maitournam, A., Simon, R.: On the efficiency of targeted clinical trials. Stat. Med. 24(3), 329–339 (2005)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Li, M.J.: Statistical methods for clinical validation of follow-on companion diagnostic devices via an external concordance study. Stat. Biopharm. Res. 8(3), 355–363 (2016)CrossRefGoogle Scholar
  3. 3.
    Li, M.J.: Statistical consideration and challenges in bridging study of personalized medicine. J. Biopharm. Stat. 25, 397–407 (2015)CrossRefGoogle Scholar
  4. 4.
    Li, M.J., Yu, T.H., Hu, Y.F.: The impact of companion diagnostic device measurement performance on clinical validation of personalized medicine. Stat. Med. 34, 2222–2234 (2015)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Lu, Y., Dendukuri, N., Schiller, I., Joseph, L.: A Bayesian approach to simultaneously adjusting for verification and reference standard bias in diagnostic test studies. Stat. Med. 29(24), 2532–2543 (2010)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Zhou, X.-H., Obuchowski, N.A., McClish, D.K.: Statistical methods in diagnostic medicine, 2nd edn. Wiley, Hoboken (2011)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Ventana Medical Systems, Inc.TucsonUSA
  2. 2.HTG Molecular DiagnosticsTucsonUSA

Personalised recommendations