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In fdb: Frequentist Dynamic Borrowing for Hybrid-Control Survival Trials

fdb: Frequentist Dynamic Borrowing for Hybrid-Control Survival Trials

Description

Implements likelihood-informed frequentist dynamic borrowing methods for hybrid-control survival trials based on penalized Cox partial likelihood estimation, together with design-stage lambda calibration and a simulation harness for evaluating operating characteristics.

Main user-facing functions

• simulate_hybrid_cox: Generate a hybrid-control Cox proportional hazards dataset.

• fit_all_methods: Fit all borrowing methods on a single dataset, returning both model-based and sandwich-based inference quantities.

• fit_one_penalized_method: Fit a single penalized borrowing method.

• run_simulation: Run a Monte Carlo simulation under a fixed scenario, comparing all methods.

• calibrate_lambda_grid, calibrate_lambda_grid_two_stage, calibrate_all_lambdas: Design-stage lambda calibration utilities.

• run_fdb_study: One-stop wrapper that performs lambda calibration and then evaluates type I error and power curves across population drift scenarios.

Penalty methods

The package implements four likelihood-informed penalties together with the adaptive lasso comparator of Li et al. (2023):

• LiAdaptiveLasso: adaptive lasso (Li et al. 2023).

• P1_SEScaledL1: precision-weighted L1 penalty.

• P2_GatedL1: smoothed integrated-gate penalty.

• P3_SEScaledMCP: information-adaptive minimax concave penalty (MCP).

• P4_LRWeightedL1: likelihood-ratio-weighted L1 penalty.

References

Li, R., Lin, R., Huang, J., Tian, L., and Zhu, J. (2023). A frequentist approach to dynamic borrowing. Biometrical Journal 65(7), 2100406.

Zhang, C.-H. (2010). Nearly unbiased variable selection under minimax concave penalty. The Annals of Statistics 38(2), 894-942.

Andersen, P. K. and Gill, R. D. (1982). Cox's regression model for counting processes: A large sample study. The Annals of Statistics 10(4), 1100-1120.

Inference and calibration

Model-based standard errors from profiled fits treat the fitted drift as fixed. Sandwich standard errors are local plug-in approximations holding first-stage weights fixed; they do not guarantee nominal coverage. Curvature clipping modifies the variance calculation, not the fitted coefficients. Calibration targets a prespecified drift grid and threshold, with Monte Carlo error. It does not establish control between grid points or outside the grid. Effective sample size is a variance-equivalent gain and can be negative; it does not account for bias.

Author(s)

Maintainer: Yusuke Yamaguchi [e-mail]

See Also

Useful links:

• https://github.com/yamagubed/fdb

• Report bugs at https://github.com/yamagubed/fdb/issues

fdb documentation built on Oct. 4, 2026, 5:07 p.m.