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Related Experiment Video

Updated: Jun 13, 2026

The Adjuvant Efficacy of Angong Niuhuang Pill in the Treatment of Viral Encephalitis: A Meta-Analysis of Randomized Controlled Trials

The Adjuvant Efficacy of Angong Niuhuang Pill in the Treatment of Viral Encephalitis: A Meta-Analysis of Randomized Controlled Trials

Published on: April 19, 2024

A SEQUENTIAL SIGNIFICANCE TEST FOR TREATMENT BY COVARIATE INTERACTIONS.

Min Qian¹, Bibhas Chakraborty^2,3, Raju Maiti²

¹Columbia University.

Statistica Sinica

|June 12, 2026

Summary

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.

Keywords:

Double robustness forward stepwise testing m-out-of-n bootstrap non-regular asymptotics personalized medicine

Related Experiment Videos

Last Updated: Jun 13, 2026

The Adjuvant Efficacy of Angong Niuhuang Pill in the Treatment of Viral Encephalitis: A Meta-Analysis of Randomized Controlled Trials

The Adjuvant Efficacy of Angong Niuhuang Pill in the Treatment of Viral Encephalitis: A Meta-Analysis of Randomized Controlled Trials

Published on: April 19, 2024

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.