Efficient screening of predictive biomarkers for individual treatment selection

Shonosuke Sugasawa, Hisashi Noma

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)


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 et al. 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.

Original languageEnglish
Pages (from-to)249-257
Number of pages9
Issue number1
Publication statusPublished - 2021 Mar
Externally publishedYes


  • empirical Bayes
  • false discovery rate
  • optimal discovering procedure
  • propensity score

ASJC Scopus subject areas

  • Statistics and Probability
  • General Biochemistry,Genetics and Molecular Biology
  • General Immunology and Microbiology
  • General Agricultural and Biological Sciences
  • Applied Mathematics


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