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Comparing Methods to Assess Treatment Effect Heterogeneity in General Parametric Regression Models.

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Summary
This summary is machine-generated.

This study compares methods for assessing treatment effect heterogeneity in regression models. Score-residual-based methods are highlighted as practical and reliable tools for identifying treatment effect modifiers.

Keywords:
global interaction testscore residualsubgroup identificationtreatment effect modifiers

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Area of Science:

  • Biostatistics
  • Statistical modeling
  • Clinical trial analysis

Background:

  • Assessing treatment effect heterogeneity is crucial for personalized medicine.
  • Parametric regression models are commonly used but require robust methods for heterogeneity assessment.

Purpose of the Study:

  • To review and compare methods for assessing treatment effect heterogeneity within parametric regression models.
  • To emphasize and evaluate score-residual-based tests for treatment effect heterogeneity.

Main Methods:

  • Comparison of standard likelihood ratio tests, bootstrap likelihood ratio tests, and Goeman's global test.
  • Focus on score-residual-based tests for treatment effect, including variants.
  • Simulation study and illustration in a time-to-event clinical trial.

Main Results:

  • Score-residual-based methods demonstrate practical utility, flexibility, and reliability.
  • These methods effectively explore treatment effect heterogeneity and modifiers.
  • Guidance for decision-making regarding treatment effect heterogeneity is provided.

Conclusions:

  • Score-residual-based methods are recommended for assessing treatment effect heterogeneity.
  • These approaches offer valuable insights for clinical decision-making.
  • The study validates the utility of these methods in real-world clinical settings.