Evaluating Correlation-Based Metric for Surrogate Marker Qualification within a Causal Correlation Framework

Authors

  • Yue Wang,

    Corresponding author
    1. BeiGene(Beijing) Co., Ltd., No. 30 Science Park Rd, Zhong-Guan-Cun Life Science Park, Changping District, Beijing 102206, P. R. China
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  • Robin Mogg,

    1. Early Clinical Development Statistics, Merck Research Laboratories, Merck & Co., Inc., UG1CD-44, 351 North Sumneytown Pike, North Wales, PA 19454, U.S.A.
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  • Jared Lunceford

    1. Early Clinical Development Statistics, Merck Research Laboratories, Merck & Co., Inc., UG1CD-44, 351 North Sumneytown Pike, North Wales, PA 19454, U.S.A.
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email: yue.wang@beigene.com

Abstract

Summary Biomarkers play an increasing role in the clinical development of new therapeutics. Earlier clinical decisions facilitated by biomarkers can lead to reduced costs and duration of drug development. Associations between biomarkers and clinical endpoints are often viewed as initial evidence supporting the intended purpose. As a result, even though it is widely understood that correlation is not proof of a causal relationship, correlation continues to be used as a metric for biomarker qualification in practice. In this article, we introduce a causal correlation framework where two different types of correlations are defined at the individual level. We show that the correlation estimate is a composite of different components, and needs to be interpreted with caution when used for biomarker qualification to avoid misleading conclusions. Otherwise, a significant correlation can be concluded even in the absence of a true underlying association. We also show how the causal quantities of interest are testable in a crossover design and provide discussion on the challenges that exist in a parallel group setting.

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