The paper introduces a three-pillar framework comprising eight modular analyses for sensitivity analysis of borrowing assumptions in externally controlled trials, illustrated with a simulated Bayesian hybrid evidence synthesis example.
arXiv preprint arXiv:2410.11713
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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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A Practical Framework for Sensitivity Analysis in Externally Controlled Trials: An Illustration with a Bayesian Hybrid Evidence Synthesis Case Study
The paper introduces a three-pillar framework comprising eight modular analyses for sensitivity analysis of borrowing assumptions in externally controlled trials, illustrated with a simulated Bayesian hybrid evidence synthesis example.
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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.