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On the Use of Marker Strategy Design to Detect Predictive Marker Effect in Cancer Immunotherapy and Targeted Therapy

Author

Listed:
  • Yan Han

    (Indiana University)

  • Ying Yuan

    (The University of Texas MD Anderson Cancer Center)

  • Sha Cao

    (Indiana University)

  • Muyi Li

    (The Wang Yanan Institute for Studies in Economics (WISE)
    Xiamen University)

  • Yong Zang

    (Indiana University)

Abstract

The marker strategy design (MSGD) has been proposed to assess and validate predictive markers for targeted therapies and immunotherapies. Under this design, patients are randomized into two strategies: the marker-based strategy, which treats patients based on their marker status, and the non-marker-based strategy, which randomizes patients into treatments independent of their marker status in the same way as in a standard randomized clinical trial. The strategy effect is then tested by comparing the response rate between the two strategies and this strategy effect is commonly used to evaluate the predictive capability of the markers. We show that this commonly used between-strategy test is flawed, which may cause investigators to miss the opportunity to discover important predictive markers or falsely claim an irrelevant marker as predictive. Then, we propose new procedures to improve the power of the MSGD to detect the predictive marker effect. One is based on a binary response endpoint; the second is based on survival endpoints. We conduct simulation studies to compare the performance of the MSGD with the widely used marker-stratified design (MSFD). Numerical studies show that the MSGD and MSFD has comparable performance. Hence, contrary to popular belief that the MSGD is an inferior design compared with the MSFD, we conclude that using the MSGD with the proposed tests is an efficient and ethical way to find predictive markers for targeted therapies.

Suggested Citation

  • Yan Han & Ying Yuan & Sha Cao & Muyi Li & Yong Zang, 2020. "On the Use of Marker Strategy Design to Detect Predictive Marker Effect in Cancer Immunotherapy and Targeted Therapy," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 12(2), pages 180-195, July.
    • Handle: RePEc:spr:stabio:v:12:y:2020:i:2:d:10.1007_s12561-019-09255-1
    DOI: 10.1007/s12561-019-09255-1
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    References listed on IDEAS

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    1. Charles Sawyers, 2004. "Targeted cancer therapy," Nature, Nature, vol. 432(7015), pages 294-297, November.
    2. Yong Zang & J. Jack Lee & Ying Yuan, 2016. "Two-stage marker-stratified clinical trial design in the presence of biomarker misclassification," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 65(4), pages 585-601, August.
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