Package {selSurv}

Selective inference workflows for penalized Cox models

Description

Research workflows for Cox-Lasso fitting, refitting, debiasing, bootstrap intervals, split-sample inference, and procedure-level re-selection diagnostics.

Details

The package distinguishes full-model/debiased, fixed-support, and procedure-level zero-padded coefficient targets.

Compare selection diagnostics across bootstrap workflows

Description

Combines the diagnostics tables returned by coxlasso_boot_parametric() for fixed-support, fixed-lambda re-selection, and retuned re-selection workflows.

Usage

compare_selection_diagnostics(
  fixed = NULL,
  reselect_fixed_lambda = NULL,
  reselect_retuned = NULL,
  digits = 2
)

Arguments

Value

A data frame with one row per term and workflow-specific re-selection and zero-frequency columns.

Unified bootstrap inference for Cox-Lasso models

Description

Computes bootstrap intervals for either an approximately debiased coefficient vector or a fixed-support Cox refit. Select the procedure with inference or type; the two routes have different inferential interpretations.

Usage

coxlasso_boot(
  object,
  inference = NULL,
  type = NULL,
  B = 500L,
  conf_level = 0.95,
  seed = NULL,
  ...
)

Arguments

Details

Supplying type = "wild" or "efron" automatically selects inference = "debiased". Supplying type = "model_based" automatically selects inference = "conditional".

Conversely, inference = "debiased" defaults to type = "wild", while inference = "conditional" defaults to type = "model_based".

For debiased bootstrap inference, a fitted object is automatically passed through coxlasso_debias() before the bootstrap engine is run. Debiasing remains independently available without bootstrap by calling coxlasso_debias(fit).

Under inference = "conditional", the support selected in the observed data is held fixed during bootstrap refitting. This does not condition on the complete selection event.

For inference = "debiased", the score bootstrap quantifies variation around the implemented one-step correction; it does not independently establish high-dimensional coverage validity.

Value

An object of class selsurv_boot. Its results element contains one selsurv_boot_method object per requested bootstrap type.

See Also

selSurv_workflows

Examples

set.seed(2026)
n <- 120L
X <- matrix(stats::rnorm(n * 4L), nrow = n)
colnames(X) <- paste0("x", seq_len(ncol(X)))
event_time <- stats::rexp(n, rate = 0.12 * exp(X[, 1] - 0.7 * X[, 2]))
censor_time <- stats::rexp(n, rate = 0.06)
time <- pmin(event_time, censor_time)
status <- as.integer(event_time <= censor_time)
fit <- coxlasso_fit(X, time, status, nfolds = 3, lambda_choice = "min")

# Illustrative B only; use many more draws for inference.
db <- coxlasso_debias(fit)
boot_score <- coxlasso_boot(fit, type = "wild", B = 8, seed = 2026)
boot_fixed <- coxlasso_boot(
  fit, inference = "conditional", B = 8, seed = 2026
)
summary(boot_fixed)

Model-based bootstrap for Cox-Lasso post-selection workflows

Description

Runs a model-based Cox bootstrap, either keeping the observed support fixed or repeating selection within each resample. Under re-selection, unselected coefficients are stored as zero. The resulting distribution describes the fitted procedure rather than an exact selection-event-conditional distribution.

Usage

coxlasso_boot_parametric(
  fit,
  B = 200,
  workflow = c(
    "fixed_support", "reselect_fixed_lambda", "reselect_retuned"
  ),
  lambda_choice = fit$lambda_choice,
  nfolds = 10,
  alpha = 0.1,
  ci_type = c("percentile", "basic"),
  seed = NULL
)

Arguments

Value

A selSurv_boot object.

Choosing between bootstrap interfaces

For fixed-support bootstrap, use coxlasso_boot(). Set inference = "conditional".

To repeat selection, use coxlasso_boot_reselect(). This interface has its own tuning and return conventions. Fits with a non-Lasso mixing parameter can be processed, but a warning is issued because these settings have not been assessed separately for inference. Here alpha is the significance level, not the penalty mixing parameter.

See Also

selSurv_workflows

Model-based Cox bootstrap with re-selection

Description

Uses a model-based Cox bootstrap to compare fixed-support refitting with procedures that repeat variable selection. An unselected coefficient is recorded as zero; failed Cox refits are recorded as missing, not as zero.

Usage

coxlasso_boot_reselect(
  x, time, status,
  B = 500L,
  methods = c(
    "fixed_support", "reselect_fixed_lambda", "reselect_cv_min"
  ),
  conf_level = 0.95,
  seed = NULL,
  nfolds = 10L,
  original = NULL,
  original_support = NULL,
  original_lambda = NULL,
  standardize = TRUE,
  alpha = 1,
  type_measure = "deviance",
  ties = "efron",
  censoring = "reverse_km",
  rng_mode = c("independent_streams", "legacy_nki70"),
  glmnet_args = list(),
  progress = interactive()
)

Arguments

Details

Re-selection characterizes variability in the selection-and-refit procedure. It does not condition on the complete selection event, and its intervals should not automatically be interpreted as confidence intervals for a single fixed coefficient.

The default RNG mode uses separate deterministic streams for bootstrap-data generation and CV folds so that results for a named method do not depend on which other methods are requested. If seed = NULL, a root seed is drawn once at the start of the call and stored in the returned object, after which the same deterministic stream construction is used. rng_mode = "legacy_nki70" is retained solely for reproducibility of the archived NKI70 B=500 analysis.

Value

An object of class selsurv_reselection and cox_reselection_bootstrap.

See Also

selSurv_workflows

Examples

set.seed(2026)
n <- 120L
X <- matrix(stats::rnorm(n * 4L), nrow = n)
colnames(X) <- paste0("x", seq_len(ncol(X)))
event_time <- stats::rexp(n, rate = 0.12 * exp(X[, 1] - 0.7 * X[, 2]))
censor_time <- stats::rexp(n, rate = 0.06)
time <- pmin(event_time, censor_time)
status <- as.integer(event_time <= censor_time)
# Illustrative B only; increase it before interpreting intervals.
rs <- coxlasso_boot_reselect(
  x = X, time = time, status = status,
  B = 8, nfolds = 3,
  methods = c(
    "fixed_support", "reselect_fixed_lambda",
    "reselect_cv_min"
  ),
  conf_level = 0.95, seed = 2026, progress = FALSE
)
summary(rs)$diagnostics

Approximate debiasing for a Cox-Lasso fit

Description

Applies a one-step Cox score correction using a ridge-stabilized inverse information matrix. This research implementation does not supply a general high-dimensional validity guarantee. It requires a Lasso pilot (fit$alpha = 1); other penalties are rejected.

Usage

coxlasso_debias(fit, ridge = 1e-04)

Arguments

Details

The score calculation warrants additional scrutiny for tied event times. Results from such data should be treated cautiously.

Value

A selSurv_debias object.

See Also

selSurv_workflows

Event-level bootstrap for debiased Cox-Lasso score contributions

Description

Event-level bootstrap for debiased Cox-Lasso score contributions

Usage

coxlasso_efron_events(db, B = 500, alpha = 0.1, seed = NULL)

Arguments

Details

The debiased estimator must originate from a Cox-Lasso fit with penalty mixing parameter alpha = 1.

Value

A selSurv_boot object.

Fit a Cox-Lasso workflow

Description

Fits a penalized Cox model with cross-validated tuning and stores the selected model. The default mixing parameter (alpha = 1) yields the Lasso.

Usage

coxlasso_fit(
  X,
  time,
  status,
  lambda_choice = c("min", "1se"),
  nfolds = 10,
  alpha = 1,
  standardize = TRUE,
  ...
)

Arguments

Value

A selSurv_fit object.

Examples

set.seed(2026)
n <- 120L
X <- matrix(stats::rnorm(n * 4L), nrow = n)
colnames(X) <- paste0("x", seq_len(ncol(X)))
event_time <- stats::rexp(n, rate = 0.12 * exp(X[, 1] - 0.7 * X[, 2]))
censor_time <- stats::rexp(n, rate = 0.06)
time <- pmin(event_time, censor_time)
status <- as.integer(event_time <= censor_time)
fit <- coxlasso_fit(X, time, status, nfolds = 3, lambda_choice = "min")
fit$selected

Refit Cox model on selected support

Description

Refit Cox model on selected support

Usage

coxlasso_refit(
  fit,
  support = fit$support,
  X = fit$X,
  time = fit$time,
  status = fit$status
)

Arguments

Value

A list with support-specific and zero-padded estimates.

Split-sample inference after Cox-Lasso selection

Description

Uses one subsample to choose predictors and a disjoint subsample to estimate coefficients and form Wald intervals.

Usage

coxlasso_split(
  X,
  time,
  status,
  split_fraction = 0.5,
  seed = NULL,
  lambda_choice = c("min", "1se"),
  nfolds = 10,
  conf_level = 0.95,
  penalty_alpha = 1,
  ...
)

Arguments

Value

A selSurv_split object.

Multiplier wild bootstrap for debiased Cox-Lasso estimator

Description

Multiplier wild bootstrap for debiased Cox-Lasso estimator

Usage

coxlasso_wildboot(
  db,
  B = 500,
  alpha = 0.1,
  type = c("percentile"),
  seed = NULL
)

Arguments

Details

The debiased estimator must originate from a Cox-Lasso fit with penalty mixing parameter alpha = 1.

Value

A selSurv_boot object.

Plot selection diagnostics across bootstrap workflows

Description

Displays re-selection or zero frequencies as dot plots or grouped bars.

Supply a combined selection-diagnostics table (see compare_selection_diagnostics()).

Usage

plot_selection_diagnostics(
  diagnostics,
  metric = c("reselect", "zero"),
  plot_type = c("dot", "bar"),
  workflows = NULL,
  sort_by = NULL,
  decreasing = TRUE,
  palette = c("color", "grey"),
  ylim = c(0, 1),
  xlim = c(0, 1),
  main = NULL,
  xlab = NULL,
  ylab = NULL,
  legend = TRUE,
  legend_position = c(
    "top_panel", "inside", "none", "side"
  ),
  colors = NULL,
  pch = c(16, 17, 15, 18),
  cex_points = 1.1,
  cex_names = 0.85,
  cex_legend = 0.85,
  las = 2,
  ...
)

Arguments

Value

Invisibly returns the matrix used for plotting.

Print methods for selSurv objects

Description

Print methods for objects returned by coxlasso_fit(), coxlasso_boot_parametric(), coxlasso_debias(), and coxlasso_split().

Usage

## S3 method for class 'selSurv_fit'
print(x, ...)

## S3 method for class 'selSurv_boot'
print(x, ...)

## S3 method for class 'selSurv_debias'
print(x, ...)

## S3 method for class 'selSurv_split'
print(x, ...)

Arguments

Value

The input object x, invisibly.

List implemented package methods

Description

List implemented package methods

Usage

selSurv_methods()

Value

A data frame with methods, targets, and status.

Explain the implemented inferential targets

Description

Explain the implemented inferential targets

Usage

selSurv_targets()

Value

A data frame describing the package workflows and targets.

Choosing an inference method in selSurv

Description

A guide to inference after Cox-Lasso variable selection.

Start with coxlasso_fit(). Subsequent methods differ in the coefficient or procedure they describe and in whether variable selection is repeated during resampling.

Details

These methods address different inferential questions. An interval's interpretation depends on the target and the procedure used to construct it.

Which function should I use?

• Approximate full-model-coordinate inference. Use coxlasso_debias() for a ridge-stabilized one-step correction. Optionally use coxlasso_boot() with inference = "debiased" and either type = "wild" or type = "efron" for a score-level bootstrap.

  This is an approximate, research-stage method. Neither the one-step correction nor the bootstrap alone establishes general high-dimensional confidence-interval validity.

• Fixed-support model-based bootstrap. Use coxlasso_boot() with inference = "conditional". The default bootstrap type is "model_based". The original selected support is held fixed throughout bootstrap refitting.

  In this interface, "conditional" means that the observed support is kept fixed. No conditioning on the complete selection event is performed.

• Procedure-level re-selection. Use coxlasso_boot_reselect() to compare fixed-support refitting with repeated Cox-Lasso selection. Available procedures include:

  • "fixed_support": retain the original support;

  • "reselect_fixed_lambda": reselect with a fixed penalty;

  • "reselect_cv_min": reselect using CV-min;

  • "reselect_cv_1se": reselect using the one-SE rule;

  • "reselect_aic": reselect by AIC.

  The reported bootstrap vectors assign zero to unselected coordinates; failures remain missing. Selection frequencies and model overlap describe stability. Resulting intervals need not have standard fixed-parameter coverage interpretations.

Relation to the separate parametric interface

coxlasso_boot_parametric() provides a separate model-based interface, including an option to retune the penalty. Its arguments, interval construction, and results differ from those of the recommended interfaces above. Use it when its specific procedure is needed; the functions are not aliases.

Meaning of alpha and confidence-level arguments

• In coxlasso_fit(), alpha is the elastic-net mixing parameter. alpha = 1 corresponds to Lasso.

• In coxlasso_boot_reselect(), alpha is also the mixing parameter.

• In coxlasso_split(), penalty_alpha is the mixing parameter; conf_level specifies the confidence level.

• In coxlasso_boot_parametric(), alpha denotes the significance level, rather than the mixing parameter.

• The lower-level score bootstrap functions also use alpha for the significance level.

Use named arguments when moving between these interfaces.

Scope and tied event times

The methods have been evaluated primarily for Lasso selection (alpha = 1). coxlasso_fit() permits other mixing parameters. The debiasing and score-bootstrap procedures require a Lasso pilot. Other inference routes may run with non-Lasso penalties, but those settings have not been evaluated separately.

Selection and unpenalized refitting can use different methods for handling tied event times. In particular, the current one-step score calculation has not been established to agree with the intended Cox score when failures are tied. Interpret debiased results for tied times with caution.

The Efron event-score bootstrap resamples event contributions; it should not be confused with the Efron partial-likelihood approximation for tied event times.

See Also

• coxlasso_fit()

• coxlasso_debias()

• coxlasso_boot()

• coxlasso_boot_reselect()

• coxlasso_boot_parametric()

• coxlasso_split()