TMLE-PR and A-TMLE borrow information from non-subgroup participants in RCTs to improve efficiency of subgroup-specific treatment effect estimation, demonstrated on Black and Asian subgroups in the LEADER trial.
Adaptive debiased machine learning using data-driven model selection techniques.arXiv preprint arXiv:2307.12544,
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Loss-weighted targeting in TMLE introduces more systematic bias than clever-covariate-scaled targeting under positivity stress, while a proposed Lepski-type adaptive truncation with brake improves stability over fixed rules like c/(sqrt(n) log n) with c=5 or 6.
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Improving the Efficiency of Subgroup Analysis in Randomized Controlled Trials with TMLE
TMLE-PR and A-TMLE borrow information from non-subgroup participants in RCTs to improve efficiency of subgroup-specific treatment effect estimation, demonstrated on Black and Asian subgroups in the LEADER trial.
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Investigating Targeting Strategies and Truncation in TMLE for the Average Treatment Effect under Practical Positivity Violations
Loss-weighted targeting in TMLE introduces more systematic bias than clever-covariate-scaled targeting under positivity stress, while a proposed Lepski-type adaptive truncation with brake improves stability over fixed rules like c/(sqrt(n) log n) with c=5 or 6.