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A SEQUENTIAL SIGNIFICANCE TEST FOR TREATMENT BY COVARIATE INTERACTIONS

Min Qian1, Bibhas Chakraborty2,3, Raju Maiti2

  • 1Columbia University.

Statistica Sinica
|June 12, 2026
PubMed

Insights

This study introduces a new hypothesis testing method using m-out-of-n bootstrap for personalized medicine. It effectively identifies treatment-covariate interactions, outperforming existing methods in simulations.

Area of Science:

  • Biomedical research
  • Clinical research
  • Statistics

Background:

  • Biomedical research is shifting towards personalized medicine.
  • Identifying treatment-covariate interactions is crucial for personalized medicine.
  • Existing machine learning methods lack formal hypothesis testing for treatment selection.

Purpose of the Study:

  • To develop a novel hypothesis testing procedure for identifying treatment-covariate interactions.
  • To address the limitations of existing machine learning methods in formal hypothesis testing.
  • To enable sequential identification of variables interacting with treatments.

Main Methods:

  • A novel testing procedure based on the m-out-of-n bootstrap.
  • Theoretical analysis of the method's properties.
  • Simulation studies to compare performance against competing methods.

Main Results:

  • The proposed m-out-of-n bootstrap method demonstrates superior control of type-I error rates.
  • The method achieves satisfactory statistical power in identifying interactions.
  • The procedure effectively identifies variables that interact with treatments.

Conclusions:

  • The novel m-out-of-n bootstrap testing procedure is a valuable tool for personalized medicine.
  • The method offers improved performance over existing approaches for treatment-covariate interaction identification.
  • The procedure's utility is confirmed through real-world data from clinical trials and observational studies.
Keywords:
Double robustnessforward stepwise testingm-out-of-n bootstrapnon-regular asymptoticspersonalized medicine

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