The fixed-support model-based bootstrap quantifies uncertainty for the unpenalized Cox refit on the support selected in the observed data. The observed support is retained throughout bootstrap analysis.
fixed <- coxlasso_boot(
fit,
inference = "conditional",
B = 500,
conf_level = 0.95,
seed = 2026
)
summary(fixed)Here, inference = "conditional" means that the selected support is held fixed as the working model. It does not denote formal conditioning on the complete Lasso selection event.
The model-based generator uses the observed selected-model Cox refit, a Breslow estimate of the cumulative baseline hazard, and an estimated censoring distribution. Bootstrap event and censoring outcomes are generated from this plug-in model, followed by an unpenalized Cox refit on the same support in every replicate.
The underlying methodological paper establishes first-order validity under Cox regularity, sparse-model, and support-recovery assumptions. The software does not claim an exact finite-sample selective guarantee when support selection remains unstable.
Report the selected support, coefficient estimates, interval construction, bootstrap size B, tie handling, censoring model, and any convergence diagnostics together with the intervals.