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

Yan Han, Ying Yuan, Sha Cao, Muyi Li and Yong Zang ()
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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)
Yong Zang: Indiana University

Statistics in Biosciences, 2020, vol. 12, issue 2, No 7, 180-195

Abstract: 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.

Keywords: Immunotherapy; Clinical trial; Biomarker; Personalized medicine (search for similar items in EconPapers)
Date: 2020
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DOI: 10.1007/s12561-019-09255-1

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