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Review: Data-driven methodology for detecting treatment effect heterogeneity

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TEHReview

Review: Data-driven methodology for detecting treatment effect heterogeneity

Collaboration: UCL MRC-CTU and the Alan Turing Institute

Interest:

  • Test for the presence of differential effects
  • Identify subgroups that represent varied responses to treatment
  • Estimate the effects for the determined patient subpopulations
  • Characterise such subpopulations

Task: Comprehensive Simulation Study

Themes:

  • Tree based methods
  • Non tree based methods
  • Metalearners