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.

Version:	0.2.0
Depends:	R (≥ 3.6.0)
Imports:	survival, stats, parallel, utils
Suggests:	testthat (≥ 3.0.0), knitr, rmarkdown
Published:	2026-10-04
DOI:	10.32614/CRAN.package.fdb
Author:	Yusuke Yamaguchi [aut, cre]
Maintainer:	Yusuke Yamaguchi <yamagubed at gmail.com>
BugReports:	https://github.com/yamagubed/fdb/issues
License:	MIT + file LICENSE
URL:	https://github.com/yamagubed/fdb
NeedsCompilation:	no
Materials:	README, NEWS
CRAN checks:	fdb results

Documentation:

Reference manual:	fdb.html , fdb.pdf
Vignettes:	Getting started with fdb (source, R code)

Downloads:

Package source:	fdb_0.2.0.tar.gz
Windows binaries:	r-devel: not available, r-release: not available, r-oldrel: fdb_0.2.0.zip
macOS binaries:	r-release (arm64): fdb_0.2.0.tgz, r-oldrel (arm64): fdb_0.2.0.tgz, r-release (x86_64): fdb_0.2.0.tgz, r-oldrel (x86_64): fdb_0.2.0.tgz

Linking:

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