A fixed-support model-based bootstrap keeps the support selected in the observed data fixed throughout bootstrap analysis. Procedure-level re-selection instead repeats Cox-Lasso selection and subsequent refitting in each bootstrap sample and therefore describes the repeated data-adaptive procedure.
In coxlasso_boot(inference = "conditional"), the term conditional means that the observed selected support is retained as the working model; it does not denote formal conditioning on the complete selection event. Re-selection is therefore exposed separately through coxlasso_boot_reselect().
The interface supports fixed_support, reselect_fixed_lambda, reselect_cv_min, reselect_cv_1se, and reselect_aic. The first three form the main comparison; CV-1SE and AIC are tuning sensitivities.
For a coordinate that is not selected in a bootstrap sample, the coefficient is recorded as zero on the original p-dimensional scale. A failed non-empty Cox refit is different: it is recorded as missing and contributes to the refit-success diagnostic rather than being treated as non-selection.
set.seed(2026)
n <- 100
p <- 6
X <- matrix(rnorm(n * p), n, p)
colnames(X) <- paste0("x", seq_len(p))
beta <- c(0.8, -0.6, 0.4, 0, 0, 0)
T <- rexp(n, rate = 0.06 * exp(drop(X %*% beta)))
C <- rexp(n, rate = 0.04)
time <- pmin(T, C)
status <- as.integer(T <= C)
library(selSurv)
fit <- coxlasso_boot_reselect(
X, time, status,
B = 50,
methods = c("fixed_support", "reselect_fixed_lambda", "reselect_cv_min"),
seed = 2026,
nfolds = 5,
standardize = TRUE
)
fit_summary <- summary(fit)
fit_summary$diagnostics
fit_summary$intervalsFor publication-scale analyses, use a larger B; small values are appropriate only for demonstrating the interface.
rng_mode = "independent_streams" is the normal default and makes results for a named method invariant to the order in which methods are requested. rng_mode = "legacy_nki70" is retained only to reproduce the archived NKI70 production analysis and is not recommended for new analyses.