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type: software
license: MIT
title: 'fdb: Frequentist Dynamic Borrowing for Hybrid-Control Survival Trials'
version: 0.2.2
doi: 10.32614/CRAN.package.fdb
abstract: 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, <https://doi.org/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.
authors:
- family-names: Yamaguchi
  given-names: Yusuke
  email: yamagubed@gmail.com
repository: https://yamagubed.r-universe.dev
repository-code: https://github.com/yamagubed/fdb
commit: dbbb2acd9744b01192beaede971dc8c81dc8d916
url: https://github.com/yamagubed/fdb
date-released: '2026-10-09'
contact:
- family-names: Yamaguchi
  given-names: Yusuke
  email: yamagubed@gmail.com