fit_li_adaptive_lasso: Adaptive lasso borrowing of Li et al. (2023)

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

Adaptive lasso borrowing of Li et al. (2023)

Description

Implements the adaptive lasso borrowing penalty p_\lambda(\delta) = \lambda |\hat\delta_0|^{-\gamma} |\delta|, where \hat\delta_0 is the unpenalized maximum partial likelihood estimator of the drift parameter.

Usage

fit_li_adaptive_lasso(
  dat,
  xnames = NULL,
  lambda,
  gamma = 1,
  delta_bounds = DEFAULT_DELTA_BOUNDS,
  full_fit = NULL,
  robust = FALSE,
  eps = SMOOTH_EPS
)

Arguments

dat	A data frame from simulate_hybrid_cox or conforming to its column conventions.
xnames	Character vector of covariate names. If NULL, automatically detected.
lambda	Penalty strength (\lambda \ge 0).
gamma	Adaptive-weight exponent (typically 1).
delta_bounds	Numeric vector of length 2 giving the optimization interval for \delta.
full_fit	Optional pre-computed full Cox fit (output of an internal helper). If NULL, the fit is computed.
robust	Use robust (Lin-Wei) Cox standard errors.
eps	Smoothing parameter for |\delta|_\varepsilon.

Value

A list of estimates and inference quantities, including the effective penalty weight w, the initial estimator delta0_hat, and its standard error se_delta.

References

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

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