fdb: Frequentist Dynamic Borrowing for Hybrid-Control Survival Trials
Implements a class of likelihood-informed frequentist dynamic
borrowing methods for hybrid-control survival trials based on penalized
Cox partial likelihood estimation. Implements four
likelihood-informed penalty structures (precision-weighted L1,
smoothed integrated-gate, information-adaptive minimax concave penalty
(MCP), and likelihood-ratio-weighted L1), together with the adaptive
lasso borrowing approach of Li et al. (2023, <doi:10.1002/bimj.202100406>).
Provides conditional model-based standard errors and local plug-in
sandwich variance approximations, with smoothed penalties. Tools for design-stage lambda calibration
via simulation, including a two-stage coarse-fine grid search, drift-level
early stopping, and per-method tuning under both inference types, are also
provided. A simulation harness for evaluating type I error and statistical
power across population drift scenarios is included.
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