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Subgroups from regression trees with adjustment for prognostic effects and postselection inference.

Wei-Yin Loh1, Michael Man2, Shuaicheng Wang3

  • 1University of Wisconsin-Madison, Madison, WI 53706, U.S.A.

Statistics in Medicine
|April 20, 2018
PubMed
Summary
This summary is machine-generated.

This study introduces methods to identify patient subgroups with different treatment effects in clinical trials. It addresses confounding and statistical significance, improving subgroup analysis reliability.

Keywords:
bootstrapdifferential treatment effectmissing valueprecision medicine

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Area of Science:

  • Biostatistics
  • Clinical Trials
  • Data Science

Background:

  • Identifying patient subgroups with differential treatment effects is crucial for personalized medicine.
  • Regression tree algorithms are commonly used but face challenges with confounding and statistical significance.

Purpose of the Study:

  • To address confounding between subgroup treatment effects and prognostic variables.
  • To develop methods for determining the statistical significance of treatment effects within identified subgroups.

Main Methods:

  • Incorporating linear prognostic effects into regression tree models to mitigate confounding.
  • Utilizing bootstrap calibration of t-intervals for reliable post-selection inference and confidence intervals.
  • Implementing these methods in the GUIDE algorithm for handling missing data and censored outcomes.

Main Results:

  • The GUIDE algorithm effectively handles missing covariate values, multiple treatment arms, and right-censored outcomes.
  • Bootstrap calibration provides accurate confidence intervals for subgroup treatment effects, applicable beyond regression trees.
  • Demonstrated utility in real-world diabetes and breast cancer trials.

Conclusions:

  • The proposed methods enhance the validity and reliability of subgroup identification in clinical trials.
  • These techniques improve the ability to detect and confirm differential treatment effects across patient subgroups.
  • The GUIDE algorithm and bootstrap calibration offer robust solutions for complex clinical trial data analysis.