Efficient screening of predictive biomarkers for individual treatment selection

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  • Shonosuke Sugasawa
    Center for Spatial Information Science The University of Tokyo Kashiwa Chiba Japan
  • Hisashi Noma
    Department of Data Science The Institute of Statistical Mathematics Tachikawa Tokyo Japan

説明

<jats:title>Abstract</jats:title><jats:p>The development of molecular diagnostic tools to achieve individualized medicine requires identifying predictive biomarkers associated with subgroups of individuals who might receive beneficial or harmful effects from different available treatments. However, due to the large number of candidate biomarkers in the large‐scale genetic and molecular studies, and complex relationships among clinical outcome, biomarkers, and treatments, the ordinary statistical tests for the interactions between treatments and covariates have difficulties from their limited statistical powers. In this paper, we propose an efficient method for detecting predictive biomarkers. We employ weighted loss functions of Chen<jats:italic>et al</jats:italic>. to directly estimate individual treatment scores and propose synthetic posterior inference for effect sizes of biomarkers. We develop an empirical Bayes approach, namely, we estimate unknown hyperparameters in the prior distribution based on data. We then provide efficient screening methods for the candidate biomarkers via optimal discovery procedure with adequate control of false discovery rate. The proposed method is demonstrated in simulation studies and an application to a breast cancer clinical study in which the proposed method was shown to detect the much larger numbers of significant biomarkers than existing standard methods.</jats:p>

収録刊行物

  • Biometrics

    Biometrics 77 (1), 249-257, 2020-04-27

    Oxford University Press (OUP)

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