An adaptive influence-function framework selects optimal external control subsets to minimize MSE of the ATE estimator in RCTs and adds outcome calibration for better data use.
arXiv preprint arXiv:2501.17835 , year=
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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.
Integration of RCTs and real-world data through explicit causal frameworks can yield evidence that is internally credible and externally relevant for individualized treatment decisions.
A review organizes externally controlled trial methodology through causal estimands and identifiability assumptions for single-arm and hybrid designs with borrowing strategies.
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Improving Treatment Effect Estimation in Trials through Adaptive Borrowing of External Controls
An adaptive influence-function framework selects optimal external control subsets to minimize MSE of the ATE estimator in RCTs and adds outcome calibration for better data use.
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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.
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Considerations for the Integration of Randomized Controlled Trials and Real-World Data
Integration of RCTs and real-world data through explicit causal frameworks can yield evidence that is internally credible and externally relevant for individualized treatment decisions.
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Externally Controlled Trials: A Review of Design and Borrowing Through a Causal Lens
A review organizes externally controlled trial methodology through causal estimands and identifiability assumptions for single-arm and hybrid designs with borrowing strategies.