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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

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Review: Data-driven methodology for detecting treatment effect heterogeneity

machine-learning inference variable-selection power clinical-trials simulations causal-inference treatment-effects false-discovery-rate subgroup-identification treatment-interaction

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