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Subgroup Testing in the Change-Plane Cox Model.

Xiao Zhang1, Panpan Ren2, Xingjie Shi3

  • 1School of Data Science, The Chinese University of Hong Kong, Shenzhen, China.

Statistics in Medicine
|July 15, 2025
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Summary
This summary is machine-generated.

This study introduces a new likelihood ratio test for change-plane Cox models, improving power for identifying treatment effect variations in patient subgroups. The method enhances survival data analysis, especially in small sample situations.

Keywords:
Cox modelcensored datalikelihood ratioprecision medicine

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Area of Science:

  • Biostatistics
  • Epidemiology
  • Oncology

Background:

  • Survival outcomes are critical in biomedical and epidemiological research.
  • Treatment effects can differ across patient subgroups, influenced by covariates like tumor mutational burden.
  • Change-plane Cox models identify subgroups with differential treatment effects in survival data.

Purpose of the Study:

  • To introduce a novel likelihood ratio test for change-plane Cox models.
  • To enhance the power of detecting treatment effect modifications in survival analysis, particularly in small samples.
  • To provide a more robust statistical method for subgroup analysis in clinical studies.

Main Methods:

  • Development of a new test statistic based on the likelihood ratio test.
  • Establishment of asymptotic distributions for the test statistic under null and local alternative hypotheses.
  • Extensive simulation studies to evaluate finite sample performance.
  • Application to real-world nonsmall cell lung cancer data.

Main Results:

  • The proposed likelihood ratio test demonstrates enhanced power compared to existing score test methods.
  • Asymptotic properties of the test statistic are theoretically established.
  • Simulation studies confirm the method's effectiveness and reliability in various scenarios.
  • The test successfully identified relevant patterns in nonsmall cell lung cancer survival data.

Conclusions:

  • The novel likelihood ratio test offers a powerful and practical approach for change-plane Cox models.
  • This method improves the identification of subgroups with varied treatment effects in survival data.
  • The approach has significant utility in analyzing complex clinical trial and epidemiological data, including cancer research.