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Midwest Biopharmaceutical Statistics Workshop

MBSW 2016: Pharmaceutical Statistics pp 183–201Cite as

Clinical Trial Designs to Evaluate Predictive Biomarkers: What’s Being Estimated?

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Part of the book series: Springer Proceedings in Mathematics & Statistics ((PROMS,volume 218))

Abstract

Predictive biomarkers are used to predict whether a patient is likely to receive benefits from a therapy that outweigh its risks. In practice, a predictive biomarker is measured with a diagnostic assay or test kit. Usually the test has some potential for measuring the biomarker with error. For qualitative tests indicating presence or absence of a biomarker, the probability of misclassification is usually not zero. Study designs to evaluate predictive biomarkers include the biomarker-stratified design, the biomarker-strategy design, the enrichment (or targeted) design, and the discordant risk randomization design. Many authors have reviewed the main strengths and weaknesses of these study designs. However, the estimand being used to evaluate the performance of the predictive biomarker is usually not provided explicitly. In this chapter, we provide explicit formulas for the estimands used in common study designs assuming that the misclassification error of the biomarker test is non-differential to outcome. The estimands are expressed as terms of the biomarker’s predictive capacity (differential in treatment effect between biomarker positive and negative patients when the biomarker is never misclassified) and the test’s predictive accuracy (e.g., positive and negative predictive values of the test for the biomarker). Upon inspection, the estimands reveal not only well-known strengths and weaknesses of the study designs, but other insights. In particular, for the biomarker-stratified design, the estimand is the product of the biomarker predictive capacity and an attenuation factor between 0 and 1 that increases with the test’s predictive accuracy. For other designs, the estimands illuminate important limitations in evaluating the clinical utility of the biomarker test. After presenting the theoretical estimands, we present and discuss estimand values for a hypothetical case study of Procalcitonin (PCT) as a biomarker in Procalcitonin-guided evaluation and management of subjects suspected of lower respiratory tract infection.

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Acknowledgements

The authors gratefully thank Drs. Qin Li from and Thomas Gwise from the Food and Drug Administration for the helpful discussions and the reviewers of our draft manuscript for their comments and suggestions.

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Authors and Affiliations

  1. Division of Biostatistics, Office of Surveillance and Biometrics, Center for Devices and Radiological Health, US Food and Drug Administration, Silver Spring, MD, USA

    Gene Pennello

  2. Division of Biometrics V, Office of Biostatistics, Office of Translational Sciences, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, MD, USA

    Jingjing Ye

Authors
  1. Gene Pennello
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  2. Jingjing Ye
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Correspondence to Jingjing Ye .

Editor information

Editors and Affiliations

  1. Statistical Innovation and Consultation group, Takeda Pharmaceuticals, Cambridge, MA, USA

    Ray Liu

  2. Division of Biometrics VI, CDER, U.S. Food and Drug Administration , Silver Spring, MD, USA

    Yi Tsong

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© 2019 This is a U.S. government work and not under copyright protection in the U.S.; foreign copyright protection may apply

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Pennello, G., Ye, J. (2019). Clinical Trial Designs to Evaluate Predictive Biomarkers: What’s Being Estimated?. In: Liu, R., Tsong, Y. (eds) Pharmaceutical Statistics. MBSW 2016. Springer Proceedings in Mathematics & Statistics, vol 218. Springer, Cham. https://doi.org/10.1007/978-3-319-67386-8_14

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