A doubly robust estimator is developed for quantile treatment effects on long-term outcomes by integrating randomized trial data with observational data under surrogate transportability, remaining consistent if either nuisance function is correctly estimated.
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The IF-LOO variance estimator for covariate-adjusted treatment effects with binary outcomes provides appropriate type I error control in simulations, especially for rare events or small samples, with a closed-form implementation.
The adaptive influence-based borrowing framework selects subsets of external controls by influence scores and chooses the subset minimizing MSE of the treatment effect estimator.
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Double/debiased machine learning of quantile treatment effects on long-term outcomes in clinical trials
A doubly robust estimator is developed for quantile treatment effects on long-term outcomes by integrating randomized trial data with observational data under surrogate transportability, remaining consistent if either nuisance function is correctly estimated.
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Improving Variance Estimation for Covariate Adjustment with Binary Outcomes
The IF-LOO variance estimator for covariate-adjusted treatment effects with binary outcomes provides appropriate type I error control in simulations, especially for rare events or small samples, with a closed-form implementation.
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Adaptive Influence-Based Borrowing Framework for Improving Treatment Effect Estimation in RCTs Using External Controls
The adaptive influence-based borrowing framework selects subsets of external controls by influence scores and chooses the subset minimizing MSE of the treatment effect estimator.