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Exploratory subgroup identification in the heterogeneous Cox model: A relatively simple procedure.

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Summary

This study introduces forest search, a novel method for identifying patient subgroups who may experience harm or benefit from treatments in survival analysis. The approach effectively controls errors and improves accuracy in detecting treatment effect heterogeneity.

Keywords:
bootstrap bias‐correctioncensored datacross‐validationgeneralized random forestsvirtual twins

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Area of Science:

  • Biostatistics
  • Survival Analysis
  • Clinical Trial Methodology

Background:

  • Identifying patient subgroups with differential treatment effects is crucial for personalized medicine.
  • Existing methods may struggle to detect subgroups where treatment is potentially detrimental.
  • The need for flexible and robust subgroup identification methods in survival data analysis.

Purpose of the Study:

  • To propose a novel procedure, 'forest search,' for identifying subgroups with significant treatment effects, including those experiencing harm.
  • To develop a method that is simple, flexible, and applicable to survival analysis.
  • To compare the performance of the proposed method against existing approaches like virtual twins and generalized random forests.

Main Methods:

  • Screening all possible subgroups using hazard ratio thresholds indicative of harm within the Cox model framework.
  • Applying a splitting consistency criterion to identify subgroups 'maximally consistent with harm.'
  • Utilizing numerical integration for approximating type-1 error and power, and employing bootstrap bias-corrected Cox model estimation with Jackknife variance approximation.

Main Results:

  • The proposed forest search approach demonstrates favorable performance compared to virtual twins and generalized random forests.
  • The method effectively controls type-1 error for falsely identifying heterogeneity.
  • Forest search exhibits higher power and classification accuracy for substantial heterogeneous treatment effects.

Conclusions:

  • Forest search is a simple, flexible, and effective procedure for identifying subgroups with significant treatment effects, particularly those at risk of harm.
  • The method offers improved control of false discoveries and enhanced power for detecting true treatment effect heterogeneity.
  • The approach is validated through simulations and real-world applications in oncology and HIV clinical trial data.