Super Learner for conditional survival functions

Description

Orchestrates the cross-validation, metalearner optimization, and prediction for an ensemble of survival base learners.

Usage

SuperSurv(
  time,
  event,
  X,
  newdata = NULL,
  new.times,
  event.library,
  cens.library,
  id = NULL,
  verbose = FALSE,
  control = list(),
  cvControl = list(),
  obsWeights = NULL,
  metalearner = "brier",
  selection = "ensemble",
  nFolds = 10,
  parallel = FALSE
)

Arguments

Value

A list of class SuperSurv containing:

• call: The matched function call.

• event.predict: Matrix of in-sample cross-validated survival predictions.

• cens.predict: Matrix of in-sample cross-validated censoring predictions.

• event.coef: Numeric vector of optimized ensemble weights for the event.

• cens.coef: Numeric vector of optimized ensemble weights for censoring.

• event.library.predict: 3D array of cross-validated predictions from individual event learners.

• event.libraryNames: Data frame detailing the algorithms and screeners used.

• event.fitLibrary: List of the fitted base learner models (if saveFitLibrary = TRUE).

• times: The time grid used for evaluation.

Examples

if (requireNamespace("glmnet", quietly = TRUE)) {
  data("metabric", package = "SuperSurv")
  dat <- metabric[1:80, ]
  x_cols <- grep("^x", names(dat))[1:5]
  X <- dat[, x_cols, drop = FALSE]
  new.times <- seq(20, 120, by = 20)

  fit <- SuperSurv(
    time = dat$duration,
    event = dat$event,
    X = X,
    newdata = X,
    new.times = new.times,
    event.library = c("surv.coxph", "surv.ridge"),
    cens.library = c("surv.coxph"),
    control = list(saveFitLibrary = TRUE)
  )

  fit$event.library.predict
}

Control parameters for Cross-Validation in SuperSurv

Description

Control parameters for Cross-Validation in SuperSurv

Usage

SuperSurv.CV.control(
  V = 10L,
  stratifyCV = TRUE,
  shuffle = TRUE,
  validRows = NULL
)

Arguments

Value

A list of CV parameters.

Control parameters for the SuperSurv Ensemble

Description

Control parameters for the SuperSurv Ensemble

Usage

SuperSurv.control(
  max.SL.iter = 20,
  event.t.grid = NULL,
  cens.t.grid = NULL,
  saveFitLibrary = TRUE,
  initWeightAlg = "surv.coxph",
  initWeight = "censoring",
  ...
)

Arguments

Value

A list of control parameters.

Create a Tuning Grid of Survival Learners

Description

Dynamically generates custom wrapper functions for a specified base learner across a grid of hyperparameters.

Usage

create_grid(base_learner, grid_params)

Arguments

Value

A character vector of the newly generated function names.

Estimate Causal Restricted Mean Survival Time (RMST)

Description

Calculates the causal treatment effect based on the difference in Restricted Mean Survival Time (RMST) between treatment and control groups up to a specific truncation time.

Usage

estimate_marginal_rmst(fit, data, trt_col, times, tau, verbose = FALSE)

Arguments

Value

A numeric value representing the estimated causal RMST difference (Treatment - Control).

Examples

data("metabric", package = "SuperSurv")
dat <- metabric[1:80, ]
x_cols <- grep("^x", names(dat))[1:5]
X <- dat[, x_cols, drop = FALSE]
new.times <- seq(20, 120, by = 20)

fit <- SuperSurv(
  time = dat$duration,
  event = dat$event,
  X = X,
  newdata = X,
  new.times = new.times,
  event.library = c("surv.coxph", "surv.glmnet"),
  cens.library = c("surv.coxph"),
  control = list(saveFitLibrary = TRUE)
)

rmst_res <- estimate_marginal_rmst(
  fit = fit,
  data = dat,
  trt_col = "x4",
  times = new.times,
  tau = 100
)
rmst_res$ATE_RMST

IPCW Brier Score and Integrated Brier Score (IBS)

Description

Calculates the Inverse Probability of Censoring Weighted (IPCW) Brier Score over a grid of times, and computes the Integrated Brier Score (IBS) using trapezoidal integration.

Usage

eval_brier(time, event, S_mat, times, tmin = min(times), tmax = max(times))

Arguments

Value

A list containing:

• brier_scores: A numeric vector of Brier scores at each time point.

• ibs: The Integrated Brier Score over the range ⁠\code{tmin}, \code{tmax}⁠.

• times: The time grid used.

Examples

data("metabric", package = "SuperSurv")
dat <- metabric[1:40, ]
x_cols <- grep("^x", names(dat))[1:3]
X <- dat[, x_cols, drop = FALSE]
newX <- X[1:10, , drop = FALSE]
times <- seq(50, 150, by = 50)

fit <- surv.coxph(
  time = dat$duration,
  event = dat$event,
  X = X,
  newdata = newX,
  new.times = times,
  obsWeights = rep(1, nrow(dat)),
  id = NULL
)

eval_brier(
  time = dat$duration[1:10],
  event = dat$event[1:10],
  S_mat = fit$pred,
  times = times
)

Calculate Concordance Index (Harrell's or Uno's)

Description

Calculate Concordance Index (Harrell's or Uno's)

Usage

eval_cindex(time, event, S_mat, times, eval_time, method = "uno")

Arguments

Value

A numeric value representing the chosen C-index.

Examples

data("metabric", package = "SuperSurv")
dat <- metabric[1:40, ]
x_cols <- grep("^x", names(dat))[1:3]
X <- dat[, x_cols, drop = FALSE]
newX <- X[1:10, , drop = FALSE]
times <- seq(50, 150, by = 50)

fit <- surv.coxph(
  time = dat$duration,
  event = dat$event,
  X = X,
  newdata = newX,
  new.times = times,
  obsWeights = rep(1, nrow(dat)),
  id = NULL
)

eval_cindex(
  time = dat$duration[1:10],
  event = dat$event[1:10],
  S_mat = fit$pred,
  times = times,
  eval_time = 100,
  method = "uno"
)

Evaluate SuperSurv predictions on test data

Description

Computes the integrated Brier score (IBS), Uno C-index, and integrated area under the curve (iAUC) for the SuperSurv ensemble and all individual base learners.

Usage

eval_summary(
  object,
  newdata,
  time,
  event,
  eval_times,
  risk_time = stats::median(eval_times),
  verbose = FALSE
)

Arguments

Value

An object of class "SuperSurv_eval" containing benchmark metrics for the ensemble and base learners.

Examples

data("metabric", package = "SuperSurv")
dat <- metabric[1:80, ]
x_cols <- grep("^x", names(dat))[1:3]

fit <- SuperSurv(
  time = dat$duration,
  event = dat$event,
  X = dat[, x_cols, drop = FALSE],
  new.times = seq(50, 200, by = 50),
  event.library = c("surv.coxph", "surv.km"),
  cens.library = c("surv.coxph", "surv.km")
)

res <- eval_summary(
  object = fit,
  newdata = dat[, x_cols, drop = FALSE],
  time = dat$duration,
  event = dat$event,
  eval_times = seq(50, 200, by = 50)
)

res

Time-Dependent AUC and Integrated AUC

Description

Evaluates the cumulative/dynamic time-dependent AUC and integrated AUC (iAUC) using inverse probability of censoring weighting (IPCW).

Usage

eval_timeROC(time, event, S_mat, times)

Arguments

Value

A list containing the AUC_curve at each time point, the times, and the integrated AUC iAUC.

Examples

 data("metabric", package = "SuperSurv")
 dat <- metabric[1:40, ]
 x_cols <- grep("^x", names(dat))[1:3]
 X <- dat[, x_cols, drop = FALSE]
 newX <- X[1:10, , drop = FALSE]
 times <- seq(50, 150, by = 50)

 fit <- surv.coxph(
   time = dat$duration,
   event = dat$event,
   X = X,
   newdata = newX,
   new.times = times,
   obsWeights = rep(1, nrow(dat)),
   id = NULL
 )

 eval_timeROC(
   time = dat$duration[1:10],
   event = dat$event[1:10],
   S_mat = fit$pred,
   times = times
 )

Explain Predictions with Global SHAP (Kernel SHAP)

Description

Explain Predictions with Global SHAP (Kernel SHAP)

Usage

explain_kernel(
  model,
  X_explain,
  X_background,
  nsim = 20,
  only_best = FALSE,
  verbose = FALSE
)

Arguments

Value

A data.frame of class c("explain", "data.frame") containing the calculated SHAP values. The columns correspond to the covariates in X_explain.

Examples

if (requireNamespace("fastshap", quietly = TRUE) &&
    requireNamespace("glmnet", quietly = TRUE)) {
  data("metabric", package = "SuperSurv")
  dat <- metabric[1:80, ]
  x_cols <- grep("^x", names(dat))[1:5]
  X <- dat[, x_cols, drop = FALSE]
  new.times <- seq(20, 120, by = 20)

  fit <- SuperSurv(
    time = dat$duration,
    event = dat$event,
    X = X,
    newdata = X,
    new.times = new.times,
    event.library = c("surv.coxph", "surv.ridge"),
    cens.library = c("surv.coxph"),
    control = list(saveFitLibrary = TRUE)
  )

  shap_values <- explain_kernel(
    model = fit,
    X_explain = X[1:10, , drop = FALSE],
    X_background = X[11:40, , drop = FALSE],
    nsim = 5
  )

  dim(shap_values)
}

Create a Time-Dependent Survex Explainer

Description

Bridges a fitted SuperSurv ensemble or a single base learner to the survex package for Time-Dependent SHAP and Model Parts.

Usage

explain_survex(model, data, y, times, label = NULL)

Arguments

Value

An explainer object of class survex_explainer created by explain_survival, which can be passed to DALEX and survex functions for further model diagnostics and plotting.

Examples

if (requireNamespace("survex", quietly = TRUE) &&
    requireNamespace("glmnet", quietly = TRUE)) {
  data("metabric", package = "SuperSurv")
  dat <- metabric[1:80, ]
  x_cols <- grep("^x", names(dat))[1:5]
  X <- dat[, x_cols, drop = FALSE]
  times <- seq(20, 120, by = 20)
  y <- survival::Surv(dat$duration, dat$event)

  fit <- SuperSurv(
    time = dat$duration,
    event = dat$event,
    X = X,
    newdata = X,
    new.times = times,
    event.library = c("surv.coxph", "surv.ridge"),
    cens.library = c("surv.coxph"),
    control = list(saveFitLibrary = TRUE)
  )

  explainer <- explain_survex(
    model = fit,
    data = X,
    y = y,
    times = times,
    label = "SuperSurv_demo"
  )

  class(explainer)
}

Calculate Restricted Mean Survival Time (RMST)

Description

Calculate Restricted Mean Survival Time (RMST)

Usage

get_rmst(surv_matrix, times, tau)

Arguments

Value

A vector of RMST values for each patient

List Available Wrappers and Screeners in SuperSurv

Description

This function prints all built-in prediction algorithms and feature screening algorithms available in the SuperSurv package.

Usage

list_wrappers(what = "both")

Arguments

Value

An invisible character vector containing the requested function names.

Examples

list_wrappers()

METABRIC Breast Cancer Dataset

Description

A subset of the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) dataset to demonstrate the SuperSurv package. 9 Covariates: 4 gene indicators (MKI67, EGFR, PGR, and ERBB2) and 5 clinical features (age at diagnosis, and indicators for hormone treatment, radiotherapy, and chemotherapy)

Usage

metabric

Format

A data frame with the clinical and genomic variables:

duration

Survival time.

event

Event indicator (1 = event, 0 = censored).

x0

Feature x0.

x1

Feature x1.

x2

Feature x2.

x3

Feature x3.

x4

Feature x4.

x5

Feature x5.

x6

Feature x6.

x7

Feature x7.

x8

Feature x8.

Beeswarm Summary Plot for SuperSurv SHAP

Description

Beeswarm Summary Plot for SuperSurv SHAP

Usage

plot_beeswarm(shap_values, data, top_n = 10)

Arguments

Value

A ggplot object visualizing the SHAP values.

Examples

if (requireNamespace("fastshap", quietly = TRUE) &&
    requireNamespace("ggforce", quietly = TRUE) &&
    requireNamespace("glmnet", quietly = TRUE)) {
  data("metabric", package = "SuperSurv")
  dat <- metabric[1:80, ]
  x_cols <- grep("^x", names(dat))[1:5]
  X <- dat[, x_cols, drop = FALSE]
  new.times <- seq(20, 120, by = 20)

  fit <- SuperSurv(
    time = dat$duration,
    event = dat$event,
    X = X,
    newdata = X,
    new.times = new.times,
    event.library = c("surv.coxph", "surv.ridge"),
    cens.library = c("surv.coxph"),
    control = list(saveFitLibrary = TRUE)
  )

  shap_values <- explain_kernel(
    model = fit,
    X_explain = X[1:20, , drop = FALSE],
    X_background = X[21:50, , drop = FALSE],
    nsim = 5
  )

  plot_beeswarm(
    shap_values = shap_values,
    data = X[1:20, , drop = FALSE],
    top_n = 5
  )
}

Plot Longitudinal Benchmark Metrics

Description

Generates time-dependent performance curves comparing the SuperSurv ensemble against its base learners, or evaluates a single standalone learner.

Usage

plot_benchmark(
  object,
  newdata,
  time,
  event,
  eval_times,
  metrics = c("brier", "auc", "cindex"),
  verbose = FALSE
)

Arguments

Value

A combined patchwork ggplot object, or a single ggplot if only one metric is selected.

Examples

if (requireNamespace("glmnet", quietly = TRUE)) {
  data("metabric", package = "SuperSurv")
  dat <- metabric[1:120, ]
  x_cols <- grep("^x", names(dat))[1:5]
  X <- dat[, x_cols, drop = FALSE]
  eval_times <- seq(20, 120, by = 20)

  fit <- SuperSurv(
    time = dat$duration,
    event = dat$event,
    X = X,
    newdata = X,
    new.times = eval_times,
    event.library = c("surv.coxph", "surv.ranger"),
    cens.library = c("surv.coxph"),
    control = list(saveFitLibrary = TRUE)
  )

  plot_benchmark(
    object = fit,
    newdata = X,
    time = dat$duration,
    event = dat$event,
    eval_times = eval_times,
    metrics = c("brier")
  )
}

Plot Survival Calibration Curve

Description

Plot Survival Calibration Curve

Usage

plot_calibration(object, newdata, time, event, eval_time, bins = 5)

Arguments

Value

A ggplot object.

Examples

if (requireNamespace("glmnet", quietly = TRUE)) {
  data("metabric", package = "SuperSurv")
  dat <- metabric[1:120, ]
  x_cols <- grep("^x", names(dat))[1:5]
  X <- dat[, x_cols, drop = FALSE]
  eval_times <- seq(20, 120, by = 20)

  fit <- SuperSurv(
    time = dat$duration,
    event = dat$event,
    X = X,
    newdata = X,
    new.times = eval_times,
    event.library = c("surv.coxph", "surv.ridge"),
    cens.library = c("surv.coxph"),
    control = list(saveFitLibrary = TRUE)
  )

  plot_calibration(
    object = fit,
    newdata = X,
    time = dat$duration,
    event = dat$event,
    eval_time = 100,
    bins = 4
  )
}

Plot SHAP Dependence for SuperSurv

Description

Plot SHAP Dependence for SuperSurv

Usage

plot_dependence(shap_values, data, feature_name, title = NULL)

Arguments

Value

A ggplot object visualizing the SHAP values.

Examples

if (requireNamespace("fastshap", quietly = TRUE) &&
    requireNamespace("glmnet", quietly = TRUE)) {
  data("metabric", package = "SuperSurv")
  dat <- metabric[1:80, ]
  x_cols <- grep("^x", names(dat))[1:5]
  X <- dat[, x_cols, drop = FALSE]
  new.times <- seq(20, 120, by = 20)

  fit <- SuperSurv(
    time = dat$duration,
    event = dat$event,
    X = X,
    newdata = X,
    new.times = new.times,
    event.library = c("surv.coxph", "surv.ridge"),
    cens.library = c("surv.coxph"),
    control = list(saveFitLibrary = TRUE)
  )

  shap_values <- explain_kernel(
    model = fit,
    X_explain = X[1:20, , drop = FALSE],
    X_background = X[21:50, , drop = FALSE],
    nsim = 5
  )

  plot_dependence(
    shap_values = shap_values,
    data = X[1:20, , drop = FALSE],
    feature_name = colnames(X)[1]
  )
}

Plot Global Feature Importance for SuperSurv

Description

Plot Global Feature Importance for SuperSurv

Usage

plot_global_importance(
  shap_values,
  title = "SuperSurv: Ensemble Feature Importance",
  top_n = 10
)

Arguments

Value

A ggplot object visualizing the SHAP values.

Examples

if (requireNamespace("fastshap", quietly = TRUE) &&
    requireNamespace("glmnet", quietly = TRUE)) {
  data("metabric", package = "SuperSurv")
  dat <- metabric[1:80, ]
  x_cols <- grep("^x", names(dat))[1:5]
  X <- dat[, x_cols, drop = FALSE]
  new.times <- seq(20, 120, by = 20)

  fit <- SuperSurv(
    time = dat$duration,
    event = dat$event,
    X = X,
    newdata = X,
    new.times = new.times,
    event.library = c("surv.coxph", "surv.ridge"),
    cens.library = c("surv.coxph"),
    control = list(saveFitLibrary = TRUE)
  )

  shap_values <- explain_kernel(
    model = fit,
    X_explain = X[1:10, , drop = FALSE],
    X_background = X[11:40, , drop = FALSE],
    nsim = 5
  )

  plot_global_importance(shap_values, top_n = 5)
}

Plot Causal RMST Difference Over Time

Description

Generates a curve showing how the causal Restricted Mean Survival Time (RMST) difference between treatment groups evolves across a sequence of different truncation times.

Usage

plot_marginal_rmst_curve(fit, data, trt_col, times, tau_seq)

Arguments

Value

A ggplot object visualizing the causal RMST difference curve.

Examples

data("metabric", package = "SuperSurv")
dat <- metabric[1:80, ]
x_cols <- grep("^x", names(dat))[1:5]
X <- dat[, x_cols, drop = FALSE]
new.times <- seq(20, 120, by = 20)

fit <- SuperSurv(
  time = dat$duration,
  event = dat$event,
  X = X,
  newdata = X,
  new.times = new.times,
  event.library = c("surv.coxph", "surv.glmnet"),
  cens.library = c("surv.coxph"),
  control = list(saveFitLibrary = TRUE)
)

tau_grid <- seq(40, 120, by = 20)
plot_marginal_rmst_curve(
  fit = fit,
  data = dat,
  trt_col = "x4",
  times = new.times,
  tau_seq = tau_grid
)

Waterfall Plot for an Individual Patient

Description

Waterfall Plot for an Individual Patient

Usage

plot_patient_waterfall(shap_values, patient_index = 1, top_n = 10)

Arguments

Value

A ggplot object visualizing the SHAP values.

Examples

if (requireNamespace("fastshap", quietly = TRUE) &&
    requireNamespace("glmnet", quietly = TRUE)) {
  data("metabric", package = "SuperSurv")
  dat <- metabric[1:80, ]
  x_cols <- grep("^x", names(dat))[1:5]
  X <- dat[, x_cols, drop = FALSE]
  new.times <- seq(20, 120, by = 20)

  fit <- SuperSurv(
    time = dat$duration,
    event = dat$event,
    X = X,
    newdata = X,
    new.times = new.times,
    event.library = c("surv.coxph", "surv.ridge"),
    cens.library = c("surv.coxph"),
    control = list(saveFitLibrary = TRUE)
  )

  shap_values <- explain_kernel(
    model = fit,
    X_explain = X[1:10, , drop = FALSE],
    X_background = X[11:40, , drop = FALSE],
    nsim = 5
  )

  plot_patient_waterfall(
    shap_values = shap_values,
    patient_index = 1,
    top_n = 5
  )
}

Plot Predicted Survival Curves

Description

Plot Predicted Survival Curves

Usage

plot_predict(preds, eval_times, patient_idx = 1)

Arguments

Value

A ggplot object.

Examples

if (requireNamespace("glmnet", quietly = TRUE)) {
  data("metabric", package = "SuperSurv")
  dat <- metabric[1:120, ]
  x_cols <- grep("^x", names(dat))[1:5]
  X <- dat[, x_cols, drop = FALSE]
  eval_times <- seq(20, 120, by = 20)

  fit <- SuperSurv(
    time = dat$duration,
    event = dat$event,
    X = X,
    newdata = X,
    new.times = eval_times,
    event.library = c("surv.coxph", "surv.ridge"),
    cens.library = c("surv.coxph"),
    control = list(saveFitLibrary = TRUE)
  )

  preds <- predict(fit, newdata = X, new.times = eval_times)

  plot_predict(
    preds = preds,
    eval_times = eval_times,
    patient_idx = c(1, 2)
  )
}

Plot Predicted RMST vs. Observed Survival Times

Description

Evaluates the calibration of the causal RMST estimator by plotting the model's predicted RMST for each patient against their actual observed follow-up time.

Usage

plot_rmst_vs_obs(fit, data, time_col, event_col, times, tau)

Arguments

Value

A ggplot object comparing predicted RMST to observed outcomes.

Examples

data("metabric", package = "SuperSurv")
dat <- metabric[1:80, ]
x_cols <- grep("^x", names(dat))[1:5]
X <- dat[, x_cols, drop = FALSE]
new.times <- seq(20, 120, by = 20)

fit <- SuperSurv(
  time = dat$duration,
  event = dat$event,
  X = X,
  newdata = X,
  new.times = new.times,
  event.library = c("surv.coxph", "surv.glmnet"),
  cens.library = c("surv.coxph"),
  control = list(saveFitLibrary = TRUE)
)

plot_rmst_vs_obs(
  fit = fit,
  data = dat,
  time_col = "duration",
  event_col = "event",
  times = new.times,
  tau = 350
)

Survival Probability Heatmap

Description

Survival Probability Heatmap

Usage

plot_survival_heatmap(object, newdata, times)

Arguments

Value

A ggplot object visualizing the SHAP values.

Examples

if (requireNamespace("glmnet", quietly = TRUE)) {
  data("metabric", package = "SuperSurv")
  dat <- metabric[1:80, ]
  x_cols <- grep("^x", names(dat))[1:5]
  X <- dat[, x_cols, drop = FALSE]
  times <- seq(20, 120, by = 20)

  fit <- SuperSurv(
    time = dat$duration,
    event = dat$event,
    X = X,
    newdata = X,
    new.times = times,
    event.library = c("surv.coxph", "surv.ridge"),
    cens.library = c("surv.coxph"),
    control = list(saveFitLibrary = TRUE)
  )

  plot_survival_heatmap(
    object = fit,
    newdata = X[1:20, , drop = FALSE],
    times = times
  )
}

Predict method for SuperSurv fits

Description

Obtains predicted survival probabilities from a fitted SuperSurv ensemble.

Usage

## S3 method for class 'SuperSurv'
predict(object, newdata, new.times, onlySL = FALSE, threshold = 1e-04, ...)

Arguments

Value

A list containing:

• event.predict: A numeric matrix (rows = observations, columns = times) of the final predicted survival probabilities from the ensemble.

• event.library.predict: A 3D numeric array (observations x times x models) containing the individual survival predictions from each base learner.

• cens.predict: A numeric matrix of the predicted censoring probabilities.

• cens.library.predict: A 3D numeric array of the individual censoring predictions.

Examples

if (requireNamespace("glmnet", quietly = TRUE)) {
  data("metabric", package = "SuperSurv")
  dat <- metabric[1:80, ]
  x_cols <- grep("^x", names(dat))[1:5]
  X <- dat[, x_cols, drop = FALSE]
  newX <- X[1:10, , drop = FALSE]
  new.times <- seq(20, 120, by = 20)

  fit <- SuperSurv(
    time = dat$duration,
    event = dat$event,
    X = X,
    newdata = X,
    new.times = new.times,
    event.library = c("surv.coxph", "surv.ridge"),
    cens.library = c("surv.coxph"),
    control = list(saveFitLibrary = TRUE)
  )

  preds <- predict(
    object = fit,
    newdata = newX,
    new.times = new.times
  )

  dim(preds$event.predict)
}

Prediction function for AORSF

Description

Obtains predicted survivals from a fitted surv.aorsf object. Uses the native aorsf prediction engine to calculate survival directly at requested times.

Usage

## S3 method for class 'surv.aorsf'
predict(object, newdata, new.times, ...)

Arguments

Value

A numeric matrix of predicted survival probabilities, where rows correspond to the observations in newdata and columns correspond to the evaluation times in new.times.

Examples

if (requireNamespace("aorsf", quietly = TRUE)) {
  data("metabric", package = "SuperSurv")
  dat <- metabric[1:30, ]
  x_cols <- grep("^x", names(dat))[1:3]
  X <- dat[, x_cols, drop = FALSE]
  newX <- X[1:5, , drop = FALSE]
  times <- seq(50, 150, by = 50)

  fit <- surv.aorsf(
    time = dat$duration,
    event = dat$event,
    X = X,
    newdata = newX,
    new.times = times,
    obsWeights = rep(1, nrow(dat)),
    id = NULL,
    n_tree = 10,
    leaf_min_events = 2
  )

  pred <- predict(fit$fit, newdata = newX, new.times = times)
  dim(pred)
}

Prediction function for BART

Description

Obtains predicted survivals from a fitted surv.bart object. Manually expands the time grid to bypass the 10-column expectation.

Usage

## S3 method for class 'surv.bart'
predict(object, newdata, new.times, ...)

Arguments

Value

A numeric matrix of predicted survival probabilities, where rows correspond to the observations in newdata and columns correspond to the evaluation times in new.times.

Examples

if (.Platform$OS.type != "windows" &&
  requireNamespace("BART", quietly = TRUE)) {
  data("metabric", package = "SuperSurv")
  dat <- metabric[1:20, ]
  x_cols <- grep("^x", names(dat))[1:3]
  X <- dat[, x_cols, drop = FALSE]
  newX <- X[1:5, , drop = FALSE]
  times <- seq(50, 150, by = 50)

  fit <- surv.bart(
    time = dat$duration,
    event = dat$event,
    X = X,
    newdata = newX,
    new.times = times,
    ntree = 3,
    ndpost = 5,
    nskip = 5
  )

  pred <- fit$pred
  dim(pred)
}

Prediction function for CoxBoost wrapper

Description

Obtains predicted survivals from a fitted surv.coxboost object.

Usage

## S3 method for class 'surv.coxboost'
predict(object, newdata, new.times, ...)

Arguments

Value

A numeric matrix of predicted survival probabilities, where rows correspond to the observations in newdata and columns correspond to the evaluation times in new.times.

Examples

if (requireNamespace("CoxBoost", quietly = TRUE)) {
  data("metabric", package = "SuperSurv")
  dat <- metabric[1:30, ]
  x_cols <- grep("^x", names(dat))[1:3]
  X <- dat[, x_cols, drop = FALSE]
  newX <- X[1:5, , drop = FALSE]
  times <- seq(50, 150, by = 50)

  fit <- surv.coxboost(
    time = dat$duration,
    event = dat$event,
    X = X,
    newdata = newX,
    new.times = times,
    obsWeights = rep(1, nrow(dat)),
    id = NULL,
    stepno = 10,
    penalty = 50
  )

  pred <- predict(fit$fit, newdata = newX, new.times = times)
  dim(pred)
}

Prediction function for Cox regression wrapper

Description

Obtains predicted survivals from a fitted surv.coxph object.

Usage

## S3 method for class 'surv.coxph'
predict(object, newdata, new.times, ...)

Arguments

Value

A numeric matrix of predicted survival probabilities, where rows correspond to the observations in newdata and columns correspond to the evaluation times in new.times.

Examples

data("metabric", package = "SuperSurv")
dat <- metabric[1:30, ]
x_cols <- grep("^x", names(dat))[1:3]
X <- dat[, x_cols, drop = FALSE]
newX <- X[1:5, , drop = FALSE]
times <- seq(50, 150, by = 50)

fit <- surv.coxph(
  time = dat$duration,
  event = dat$event,
  X = X,
  newdata = newX,
  new.times = times,
  obsWeights = rep(1, nrow(dat)),
  id = NULL
)

pred <- predict(fit$fit, newdata = newX, new.times = times)
dim(pred)

Prediction function for GAM wrapper

Description

Obtains predicted survivals from a fitted surv.gam object.

Usage

## S3 method for class 'surv.gam'
predict(object, newdata, new.times, ...)

Arguments

Value

A numeric matrix of predicted survival probabilities, where rows correspond to the observations in newdata and columns correspond to the evaluation times in new.times.

Examples

if (requireNamespace("mgcv", quietly = TRUE)) {
  data("metabric", package = "SuperSurv")
  dat <- metabric[1:30, ]
  x_cols <- grep("^x", names(dat))[1:3]
  X <- dat[, x_cols, drop = FALSE]
  newX <- X[1:5, , drop = FALSE]
  times <- seq(50, 150, by = 50)

  fit <- surv.gam(
    time = dat$duration,
    event = dat$event,
    X = X,
    newdata = newX,
    new.times = times,
    obsWeights = rep(1, nrow(dat)),
    id = NULL,
    cts.num = 5
  )

  pred <- predict(fit$fit, newdata = newX, new.times = times)
  dim(pred)
}

Prediction function for GBM wrapper

Description

Obtains predicted survivals from a fitted surv.gbm object. Uses a step function to align the Breslow baseline hazard with the requested times.

Usage

## S3 method for class 'surv.gbm'
predict(object, newdata, new.times, ...)

Arguments

Value

A numeric matrix of predicted survival probabilities, where rows correspond to the observations in newdata and columns correspond to the evaluation times in new.times.

Examples

if (requireNamespace("gbm", quietly = TRUE)) {
  data("metabric", package = "SuperSurv")
  dat <- metabric[1:30, ]
  x_cols <- grep("^x", names(dat))[1:3]
  X <- dat[, x_cols, drop = FALSE]
  newX <- X[1:5, , drop = FALSE]
  times <- seq(50, 150, by = 50)

  fit <- surv.gbm(
    time = dat$duration,
    event = dat$event,
    X = X,
    newdata = newX,
    new.times = times,
    obsWeights = rep(1, nrow(dat)),
    id = NULL,
    n.trees = 20,
    interaction.depth = 1,
    shrinkage = 0.05,
    cv.folds = 0,
    n.minobsinnode = 3
  )

  pred <- predict(fit$fit, newdata = newX, new.times = times)
  dim(pred)
}

Prediction function for GLMNET wrapper

Description

Obtains predicted survivals from a fitted surv.glmnet object.

Usage

## S3 method for class 'surv.glmnet'
predict(object, newdata, new.times, ...)

Arguments

Value

A numeric matrix of predicted survival probabilities, where rows correspond to the observations in newdata and columns correspond to the evaluation times in new.times.

Examples

if (requireNamespace("glmnet", quietly = TRUE)) {
  data("metabric", package = "SuperSurv")
  dat <- metabric[1:30, ]
  x_cols <- grep("^x", names(dat))[1:3]
  X <- dat[, x_cols, drop = FALSE]
  newX <- X[1:5, , drop = FALSE]
  times <- seq(50, 150, by = 50)

  fit <- surv.glmnet(
    time = dat$duration,
    event = dat$event,
    X = X,
    newdata = newX,
    new.times = times,
    obsWeights = rep(1, nrow(dat)),
    id = NULL,
    alpha = 1,
    nfolds = 3
  )

  pred <- predict(fit$fit, newdata = newX, new.times = times)
  dim(pred)
}

Predict Method for Kaplan-Meier Wrapper

Description

Obtains predicted survivals from a fitted surv.km object.

Usage

## S3 method for class 'surv.km'
predict(object, newdata, new.times, ...)

Arguments

Value

A numeric matrix of predicted survival probabilities, where rows correspond to the observations in newdata and columns correspond to the evaluation times.

Examples

data("metabric", package = "SuperSurv")
dat <- metabric[1:30, ]
x_cols <- grep("^x", names(dat))[1:3]
X <- dat[, x_cols, drop = FALSE]
newX <- X[1:5, , drop = FALSE]
times <- seq(50, 150, by = 50)

fit <- surv.km(
  time = dat$duration,
  event = dat$event,
  X = X,
  newdata = newX,
  new.times = times,
  obsWeights = rep(1, nrow(dat)),
  id = NULL
)

pred <- predict(fit$fit, newdata = newX, new.times = times)
dim(pred)

Prediction function for Universal Parametric Wrapper

Description

Obtains predicted survivals from a fitted surv.parametric object using exact closed-form equations.

Usage

## S3 method for class 'surv.parametric'
predict(object, newdata, new.times, ...)

Arguments

Value

A numeric matrix of predicted survival probabilities, where rows correspond to the observations in newdata and columns correspond to the evaluation times in new.times.

Examples

data("metabric", package = "SuperSurv")
dat <- metabric[1:30, ]
x_cols <- grep("^x", names(dat))[1:3]
X <- dat[, x_cols, drop = FALSE]
newX <- X[1:5, , drop = FALSE]
times <- seq(50, 150, by = 50)

fit <- surv.parametric(
  time = dat$duration,
  event = dat$event,
  X = X,
  newdata = newX,
  new.times = times,
  obsWeights = rep(1, nrow(dat)),
  id = NULL,
  dist = "weibull"
)

pred <- predict(fit$fit, newdata = newX, new.times = times)
dim(pred)

Prediction function for Ranger wrapper

Description

Obtains predicted survivals from a fitted surv.ranger object.

Usage

## S3 method for class 'surv.ranger'
predict(object, newdata, new.times, ...)

Arguments

Value

A numeric matrix of predicted survival probabilities, where rows correspond to the observations in newdata and columns correspond to the evaluation times in new.times.

Examples

if (requireNamespace("ranger", quietly = TRUE)) {
  data("metabric", package = "SuperSurv")
  dat <- metabric[1:30, ]
  x_cols <- grep("^x", names(dat))[1:3]
  X <- dat[, x_cols, drop = FALSE]
  newX <- X[1:5, , drop = FALSE]
  times <- seq(50, 150, by = 50)

  fit <- surv.ranger(
    time = dat$duration,
    event = dat$event,
    X = X,
    newdata = newX,
    new.times = times,
    obsWeights = rep(1, nrow(dat)),
    id = NULL,
    num.trees = 10,
    min.node.size = 3
  )

  pred <- predict(fit$fit, newdata = newX, new.times = times)
  dim(pred)
}

Prediction function for survival random forest (RFSRC)

Description

Obtains predicted survivals from a fitted surv.rfsrc object.

Usage

## S3 method for class 'surv.rfsrc'
predict(object, newdata, new.times, ...)

Arguments

Value

A numeric matrix of predicted survival probabilities, where rows correspond to the observations in newdata and columns correspond to the evaluation times in new.times.

Examples

if (requireNamespace("randomForestSRC", quietly = TRUE)) {
  data("metabric", package = "SuperSurv")
  dat <- metabric[1:30, ]
  x_cols <- grep("^x", names(dat))[1:3]
  X <- dat[, x_cols, drop = FALSE]
  newX <- X[1:5, , drop = FALSE]
  times <- seq(50, 150, by = 50)

  fit <- surv.rfsrc(
    time = dat$duration,
    event = dat$event,
    X = X,
    newdata = newX,
    new.times = times,
    ntree = 10,
    nodesize = 3
  )

  pred <- predict(fit$fit, newdata = newX, new.times = times)
  dim(pred)
}

Prediction function for Ridge wrapper

Description

Obtains predicted survivals from a fitted surv.ridge object.

Usage

## S3 method for class 'surv.ridge'
predict(object, newdata, new.times, ...)

Arguments

Value

A numeric matrix of predicted survival probabilities, where rows correspond to the observations in newdata and columns correspond to the evaluation times in new.times.

Examples

if (requireNamespace("glmnet", quietly = TRUE)) {
  data("metabric", package = "SuperSurv")
  dat <- metabric[1:30, ]
  x_cols <- grep("^x", names(dat))[1:3]
  X <- dat[, x_cols, drop = FALSE]
  newX <- X[1:5, , drop = FALSE]
  times <- seq(50, 150, by = 50)

  fit <- surv.ridge(
    time = dat$duration,
    event = dat$event,
    X = X,
    newdata = newX,
    new.times = times,
    nfolds = 3
  )

  pred <- predict(fit$fit, newdata = newX, new.times = times)
  dim(pred)
}

Prediction function for rpart wrapper

Description

Obtains predicted survivals from a fitted surv.rpart object.

Usage

## S3 method for class 'surv.rpart'
predict(object, newdata, new.times, ...)

Arguments

Value

A numeric matrix of predicted survival probabilities, where rows correspond to the observations in newdata and columns correspond to the evaluation times in new.times.

Examples

if (requireNamespace("rpart", quietly = TRUE)) {
  data("metabric", package = "SuperSurv")
  dat <- metabric[1:30, ]
  x_cols <- grep("^x", names(dat))[1:3]
  X <- dat[, x_cols, drop = FALSE]
  newX <- X[1:5, , drop = FALSE]
  times <- seq(50, 150, by = 50)

  fit <- surv.rpart(
    time = dat$duration,
    event = dat$event,
    X = X,
    newdata = newX,
    new.times = times,
    obsWeights = rep(1, nrow(dat)),
    id = NULL,
    cp = 0.01,
    minsplit = 5,
    maxdepth = 3
  )

  pred <- predict(fit$fit, newdata = newX, new.times = times)
  dim(pred)
}

Prediction function for SVM wrapper

Description

Obtains predicted survivals from a fitted surv.svm object. Applies the learned Cox calibration to the new raw utility scores.

Usage

## S3 method for class 'surv.svm'
predict(object, newdata, new.times, ...)

Arguments

Value

A numeric matrix of predicted survival probabilities, where rows correspond to the observations in newdata and columns correspond to the evaluation times in new.times.

Examples

if (requireNamespace("survivalsvm", quietly = TRUE) &&
  requireNamespace("quadprog", quietly = TRUE)) {
  data("metabric", package = "SuperSurv")
  dat <- metabric[1:25, ]
  x_cols <- grep("^x", names(dat))[1:3]
  X <- dat[, x_cols, drop = FALSE]
  newX <- X[1:5, , drop = FALSE]
  times <- seq(50, 150, by = 50)

  fit <- surv.svm(
    time = dat$duration,
    event = dat$event,
    X = X,
    newdata = newX,
    new.times = times
  )

  pred <- predict(fit$fit, newdata = newX, new.times = times)
  dim(pred)
}

Prediction function for XGBoost wrapper

Description

Obtains predicted survivals from a fitted surv.xgboost object.

Usage

## S3 method for class 'surv.xgboost'
predict(object, newdata, new.times, ...)

Arguments

Value

A numeric matrix of predicted survival probabilities, where rows correspond to the observations in newdata and columns correspond to the evaluation times in new.times.

Examples

if (requireNamespace("xgboost", quietly = TRUE)) {
  data("metabric", package = "SuperSurv")
  dat <- metabric[1:30, ]
  x_cols <- grep("^x", names(dat))[1:3]
  X <- dat[, x_cols, drop = FALSE]
  newX <- X[1:5, , drop = FALSE]
  times <- seq(50, 150, by = 50)

  fit <- surv.xgboost(
    time = dat$duration,
    event = dat$event,
    X = X,
    newdata = newX,
    new.times = times,
    obsWeights = rep(1, nrow(dat)),
    id = NULL,
    nrounds = 5,
    early_stopping_rounds = 2,
    max_depth = 1
  )

  pred <- predict(fit$fit, newdata = newX, new.times = times)
  dim(pred)
}

Keep All Variables Screener

Description

Keep All Variables Screener

Usage

screen.all(X, ...)

Arguments

Value

A logical vector of the same length as the number of columns in X, indicating which variables passed the screening algorithm (TRUE to keep, FALSE to drop).

Examples

data("metabric", package = "SuperSurv")
dat <- metabric[1:20, ]
x_cols <- grep("^x", names(dat))[1:5]
X <- dat[, x_cols, drop = FALSE]

screen.all(X)

Elastic Net Screening Algorithm

Description

This screening algorithm uses cv.glmnet to select covariates. Unlike LASSO (alpha = 1), which drops correlated features, Elastic Net (alpha = 0.5 by default) shrinks correlated groups of features together, making it ideal for selecting entire biological pathways.

Usage

screen.elasticnet(
  time,
  event,
  X,
  obsWeights = NULL,
  alpha = 0.5,
  minscreen = 2,
  nfolds = 10,
  nlambda = 100,
  ...
)

Arguments

Value

A logical vector of the same length as the number of columns in X, indicating which variables passed the screening algorithm (TRUE to keep, FALSE to drop).

Examples

if (requireNamespace("glmnet", quietly = TRUE)) {
  data("metabric", package = "SuperSurv")
  dat <- metabric[1:40, ]
  x_cols <- grep("^x", names(dat))[1:5]
  X <- dat[, x_cols, drop = FALSE]

  screen.elasticnet(
    time = dat$duration,
    event = dat$event,
    X = X,
    alpha = 0.5,
    minscreen = 2,
    nfolds = 3,
    nlambda = 20
  )
}

GLMNET (Lasso) Screening

Description

GLMNET (Lasso) Screening

Usage

screen.glmnet(
  time,
  event,
  X,
  obsWeights = NULL,
  alpha = 1,
  minscreen = 2,
  nfolds = 10,
  nlambda = 100,
  ...
)

Arguments

Value

A logical vector of the same length as the number of columns in X, indicating which variables passed the screening algorithm (TRUE to keep, FALSE to drop).

Examples

if (requireNamespace("glmnet", quietly = TRUE)) {
  data("metabric", package = "SuperSurv")
  dat <- metabric[1:40, ]
  x_cols <- grep("^x", names(dat))[1:5]
  X <- dat[, x_cols, drop = FALSE]

  screen.glmnet(
    time = dat$duration,
    event = dat$event,
    X = X,
    alpha = 1,
    minscreen = 2,
    nfolds = 3,
    nlambda = 20
  )
}

Marginal Cox Regression Screening

Description

Marginal Cox Regression Screening

Usage

screen.marg(time, event, X, obsWeights = NULL, minscreen = 2, min.p = 0.1, ...)

Arguments

Value

A logical vector of the same length as the number of columns in X, indicating which variables passed the screening algorithm (TRUE to keep, FALSE to drop).

Examples

data("metabric", package = "SuperSurv")
dat <- metabric[1:40, ]
x_cols <- grep("^x", names(dat))[1:5]
X <- dat[, x_cols, drop = FALSE]

screen.marg(
  time = dat$duration,
  event = dat$event,
  X = X,
  minscreen = 2,
  min.p = 0.2
)

Random Survival Forest Screening Algorithm

Description

This screening algorithm uses the randomForestSRC package to select covariates based on their Variable Importance (VIMP). It grows a fast forest and retains features with a VIMP greater than zero.

Usage

screen.rfsrc(
  time,
  event,
  X,
  obsWeights = NULL,
  minscreen = 2,
  ntree = 100,
  ...
)

Arguments

Value

A logical vector of the same length as the number of columns in X, indicating which variables passed the screening algorithm (TRUE to keep, FALSE to drop).

Examples

if (requireNamespace("randomForestSRC", quietly = TRUE)) {
  data("metabric", package = "SuperSurv")
  dat <- metabric[1:40, ]
  x_cols <- grep("^x", names(dat))[1:5]
  X <- dat[, x_cols, drop = FALSE]

  screen.rfsrc(
    time = dat$duration,
    event = dat$event,
    X = X,
    minscreen = 2,
    ntree = 10
  )
}

High Variance Screening Algorithm (Unsupervised)

Description

An unsupervised screening algorithm that filters out low-variance features. This is particularly useful for high-dimensional genomic or transcriptomic data where many features remain relatively constant across all observations.

Usage

screen.var(
  time,
  event,
  X,
  obsWeights = NULL,
  keep_fraction = 0.5,
  minscreen = 2,
  ...
)

Arguments

Value

A logical vector of the same length as the number of columns in X, indicating which variables passed the screening algorithm (TRUE to keep, FALSE to drop).

Examples

data("metabric", package = "SuperSurv")
dat <- metabric[1:40, ]
x_cols <- grep("^x", names(dat))[1:6]
X <- dat[, x_cols, drop = FALSE]

screen.var(
  time = dat$duration,
  event = dat$event,
  X = X,
  keep_fraction = 0.5,
  minscreen = 2
)

Wrapper for AORSF (Oblique Random Survival Forest)

Description

Final Production Wrapper for AORSF (Tunable & Robust).

Usage

surv.aorsf(
  time,
  event,
  X,
  newdata,
  new.times,
  obsWeights,
  id,
  n_tree = 500,
  leaf_min_events = 5,
  mtry = NULL,
  ...
)

Arguments

Value

A list containing:

• fit: The fitted model object (e.g., the raw coxph or xgb.Booster object). If the model fails to fit, this may be an object of class try-error.

• pred: A numeric matrix of cross-validated survival predictions evaluated at the specified new.times grid.

Examples

if (requireNamespace("aorsf", quietly = TRUE)) {
  data("metabric", package = "SuperSurv")
  dat <- metabric[1:30, ]
  x_cols <- grep("^x", names(dat))[1:3]
  X <- dat[, x_cols, drop = FALSE]
  newX <- X[1:5, , drop = FALSE]
  times <- seq(50, 150, by = 50)

  fit <- surv.aorsf(
    time = dat$duration,
    event = dat$event,
    X = X,
    newdata = newX,
    new.times = times,
    obsWeights = rep(1, nrow(dat)),
    id = NULL,
    n_tree = 10,
    leaf_min_events = 2
  )

  dim(fit$pred)
}

Wrapper for BART (Bayesian Additive Regression Trees)

Description

Final Production Wrapper for BART (Tunable & Robust). Uses the mc.surv.bart function. Automatically reshapes the flat output vector into a survival matrix and interpolates the predictions to the requested new.times.

Usage

surv.bart(
  time,
  event,
  X,
  newdata = NULL,
  new.times,
  obsWeights = NULL,
  id = NULL,
  ntree = 10,
  ndpost = 30,
  nskip = 10,
  ...
)

Arguments

Value

A list containing:

• fit: The fitted model object (e.g., the raw coxph or xgb.Booster object). If the model fails to fit, this may be an object of class try-error.

• pred: A numeric matrix of cross-validated survival predictions evaluated at the specified new.times grid.

Examples

if (.Platform$OS.type != "windows" &&
  requireNamespace("BART", quietly = TRUE)) {
  data("metabric", package = "SuperSurv")
  dat <- metabric[1:20, ]
  x_cols <- grep("^x", names(dat))[1:3]
  X <- dat[, x_cols, drop = FALSE]
  newX <- X[1:5, , drop = FALSE]
  times <- seq(50, 150, by = 50)

  fit <- surv.bart(
    time = dat$duration,
    event = dat$event,
    X = X,
    newdata = newX,
    new.times = times,
    ntree = 3,
    ndpost = 5,
    nskip = 5
  )

  dim(fit$pred)
}

Wrapper function for Component-Wise Boosting (CoxBoost)

Description

Final Production Wrapper for CoxBoost (Tunable & Robust). Estimates a Cox model via component-wise likelihood based boosting.

Usage

surv.coxboost(
  time,
  event,
  X,
  newdata,
  new.times,
  obsWeights,
  id,
  stepno = 100,
  penalty = 100,
  ...
)

Arguments

Value

A list containing:

• fit: The fitted model object (e.g., the raw coxph or xgb.Booster object). If the model fails to fit, this may be an object of class try-error.

• pred: A numeric matrix of cross-validated survival predictions evaluated at the specified new.times grid.

Examples

if (requireNamespace("CoxBoost", quietly = TRUE)) {
  data("metabric", package = "SuperSurv")
  dat <- metabric[1:30, ]
  x_cols <- grep("^x", names(dat))[1:3]
  X <- dat[, x_cols, drop = FALSE]
  newX <- X[1:5, , drop = FALSE]
  times <- seq(50, 150, by = 50)

  fit <- surv.coxboost(
    time = dat$duration,
    event = dat$event,
    X = X,
    newdata = newX,
    new.times = times,
    obsWeights = rep(1, nrow(dat)),
    id = NULL,
    stepno = 10,
    penalty = 50
  )

  dim(fit$pred)
}

Wrapper for standard Cox Proportional Hazards

Description

Final Production Wrapper for CoxPH. Uses partial maximum likelihood and the Breslow estimator.

Usage

surv.coxph(time, event, X, newdata, new.times, obsWeights, id, ...)

Arguments

Value

A list containing:

• fit: The fitted model object (e.g., the raw coxph or xgb.Booster object). If the model fails to fit, this may be an object of class try-error.

• pred: A numeric matrix of cross-validated survival predictions evaluated at the specified new.times grid.

Examples

data("metabric", package = "SuperSurv")
dat <- metabric[1:30, ]
x_cols <- grep("^x", names(dat))[1:3]
X <- dat[, x_cols, drop = FALSE]
newX <- X[1:5, , drop = FALSE]
times <- seq(50, 150, by = 50)

fit <- surv.coxph(
  time = dat$duration,
  event = dat$event,
  X = X,
  newdata = newX,
  new.times = times,
  obsWeights = rep(1, nrow(dat)),
  id = NULL
)

dim(fit$pred)

Parametric Survival Prediction Wrapper (Exponential)

Description

Parametric Survival Prediction Wrapper (Exponential)

Usage

surv.exponential(time, event, X, newdata, new.times, obsWeights, id, ...)

Arguments

Value

A list containing the fitted model and predictions.

Examples

data("metabric", package = "SuperSurv")
dat <- metabric[1:30, ]
x_cols <- grep("^x", names(dat))[1:3]
X <- dat[, x_cols, drop = FALSE]
newX <- X[1:5, , drop = FALSE]
times <- seq(50, 150, by = 50)

fit <- surv.exponential(
  time = dat$duration,
  event = dat$event,
  X = X,
  newdata = newX,
  new.times = times,
  obsWeights = rep(1, nrow(dat)),
  id = NULL
)

dim(fit$pred)

Wrapper for Generalized Additive Cox Regression (GAM)

Description

Final Production Wrapper for GAM (Tunable & Robust). Uses gam to fit an additive combination of smooth and linear functions.

Usage

surv.gam(time, event, X, newdata, new.times, obsWeights, id, cts.num = 5, ...)

Arguments

Value

A list containing:

• fit: The fitted model object (e.g., the raw coxph or xgb.Booster object). If the model fails to fit, this may be an object of class try-error.

• pred: A numeric matrix of cross-validated survival predictions evaluated at the specified new.times grid.

Examples

if (requireNamespace("mgcv", quietly = TRUE)) {
  data("metabric", package = "SuperSurv")
  dat <- metabric[1:30, ]
  x_cols <- grep("^x", names(dat))[1:3]
  X <- dat[, x_cols, drop = FALSE]
  newX <- X[1:5, , drop = FALSE]
  times <- seq(50, 150, by = 50)

  fit <- surv.gam(
    time = dat$duration,
    event = dat$event,
    X = X,
    newdata = newX,
    new.times = times,
    obsWeights = rep(1, nrow(dat)),
    id = NULL,
    cts.num = 5
  )

  dim(fit$pred)
}

Wrapper function for Gradient Boosting (GBM) prediction algorithm

Description

Final Production Wrapper for GBM (Tunable & Robust). Estimates a Cox proportional hazards model via gradient boosting. Uses the Breslow estimator with a step-function approach for the baseline hazard. Includes internal safeguards against C++ crashes and small cross-validation folds.

Usage

surv.gbm(
  time,
  event,
  X,
  newdata,
  new.times,
  obsWeights,
  id,
  n.trees = 1000,
  interaction.depth = 2,
  shrinkage = 0.01,
  cv.folds = 5,
  n.minobsinnode = 10,
  ...
)

Arguments

Value

A list containing:

• fit: The fitted model object (e.g., the raw coxph or xgb.Booster object). If the model fails to fit, this may be an object of class try-error.

• pred: A numeric matrix of cross-validated survival predictions evaluated at the specified new.times grid.

Examples

if (requireNamespace("gbm", quietly = TRUE)) {
  data("metabric", package = "SuperSurv")
  dat <- metabric[1:30, ]
  x_cols <- grep("^x", names(dat))[1:3]
  X <- dat[, x_cols, drop = FALSE]
  newX <- X[1:5, , drop = FALSE]
  times <- seq(50, 150, by = 50)

  fit <- surv.gbm(
    time = dat$duration,
    event = dat$event,
    X = X,
    newdata = newX,
    new.times = times,
    obsWeights = rep(1, nrow(dat)),
    id = NULL,
    n.trees = 20,
    interaction.depth = 1,
    shrinkage = 0.05,
    cv.folds = 0,
    n.minobsinnode = 3
  )

  dim(fit$pred)
}

Wrapper function for Penalized Cox Regression (GLMNET)

Description

Final Production Wrapper for GLMNET (Tunable & Robust). Estimates a penalized Cox model (Lasso, Ridge, or Elastic Net) with automatic lambda selection. Uses the Breslow estimator with a step-function approach for the baseline hazard.

Usage

surv.glmnet(
  time,
  event,
  X,
  newdata,
  new.times,
  obsWeights,
  id,
  alpha = 1,
  nfolds = 10,
  ...
)

Arguments

Value

A list containing:

• fit: The fitted model object (e.g., the raw coxph or xgb.Booster object). If the model fails to fit, this may be an object of class try-error.

• pred: A numeric matrix of cross-validated survival predictions evaluated at the specified new.times grid.

Examples

if (requireNamespace("glmnet", quietly = TRUE)) {
  data("metabric", package = "SuperSurv")
  dat <- metabric[1:30, ]
  x_cols <- grep("^x", names(dat))[1:3]
  X <- dat[, x_cols, drop = FALSE]
  newX <- X[1:5, , drop = FALSE]
  times <- seq(50, 150, by = 50)

  fit <- surv.glmnet(
    time = dat$duration,
    event = dat$event,
    X = X,
    newdata = newX,
    new.times = times,
    obsWeights = rep(1, nrow(dat)),
    id = NULL,
    alpha = 1,
    nfolds = 3
  )

  dim(fit$pred)
}

Kaplan-Meier Prediction Algorithm

Description

This prediction algorithm ignores all covariates and computes the marginal Kaplan-Meier survival estimator using the survfit function.

Usage

surv.km(time, event, X, newdata, new.times, obsWeights, id, ...)

Arguments

Value

A list containing:

• fit: A list containing the fitted survfit object.

• pred: A numeric matrix of cross-validated survival predictions evaluated at new.times.

Examples

data("metabric", package = "SuperSurv")
dat <- metabric[1:30, ]
x_cols <- grep("^x", names(dat))[1:3]
X <- dat[, x_cols, drop = FALSE]
newX <- X[1:5, , drop = FALSE]
times <- seq(50, 150, by = 50)

fit <- surv.km(
  time = dat$duration,
  event = dat$event,
  X = X,
  newdata = newX,
  new.times = times,
  obsWeights = rep(1, nrow(dat)),
  id = NULL
)

dim(fit$pred)

Parametric Survival Prediction Wrapper (Log-Logistic)

Description

Parametric Survival Prediction Wrapper (Log-Logistic)

Usage

surv.loglogistic(time, event, X, newdata, new.times, obsWeights, id, ...)

Arguments

Value

A list containing the fitted model and predictions.

Examples

data("metabric", package = "SuperSurv")
dat <- metabric[1:30, ]
x_cols <- grep("^x", names(dat))[1:3]
X <- dat[, x_cols, drop = FALSE]
newX <- X[1:5, , drop = FALSE]
times <- seq(50, 150, by = 50)

fit <- surv.loglogistic(
  time = dat$duration,
  event = dat$event,
  X = X,
  newdata = newX,
  new.times = times,
  obsWeights = rep(1, nrow(dat)),
  id = NULL
)

dim(fit$pred)

Parametric Survival Prediction Wrapper (Log-Normal)

Description

Parametric Survival Prediction Wrapper (Log-Normal)

Usage

surv.lognormal(time, event, X, newdata, new.times, obsWeights, id, ...)

Arguments

Value

A list containing the fitted model and predictions.

Examples

data("metabric", package = "SuperSurv")
dat <- metabric[1:30, ]
x_cols <- grep("^x", names(dat))[1:3]
X <- dat[, x_cols, drop = FALSE]
newX <- X[1:5, , drop = FALSE]
times <- seq(50, 150, by = 50)

fit <- surv.lognormal(
  time = dat$duration,
  event = dat$event,
  X = X,
  newdata = newX,
  new.times = times,
  obsWeights = rep(1, nrow(dat)),
  id = NULL
)

dim(fit$pred)

Universal Parametric Survival Wrapper

Description

Final Production Wrapper for AFT Models (Weibull, Exponential, LogNormal, LogLogistic). Replaces individual wrappers with one robust, vectorized function.

Usage

surv.parametric(
  time,
  event,
  X,
  newdata,
  new.times,
  obsWeights,
  id,
  dist = "weibull",
  ...
)

Arguments

Value

A list containing:

• fit: The fitted model object (e.g., the raw coxph or xgb.Booster object). If the model fails to fit, this may be an object of class try-error.

• pred: A numeric matrix of cross-validated survival predictions evaluated at the specified new.times grid.

Examples

data("metabric", package = "SuperSurv")
dat <- metabric[1:30, ]
x_cols <- grep("^x", names(dat))[1:3]
X <- dat[, x_cols, drop = FALSE]
newX <- X[1:5, , drop = FALSE]
times <- seq(50, 150, by = 50)

fit <- surv.parametric(
  time = dat$duration,
  event = dat$event,
  X = X,
  newdata = newX,
  new.times = times,
  obsWeights = rep(1, nrow(dat)),
  id = NULL,
  dist = "weibull"
)

dim(fit$pred)

Wrapper function for Ranger Random Survival Forest

Description

Final Production Wrapper for Ranger (Tunable & Fast). Uses the ranger C++ implementation to estimate survival curves.

Usage

surv.ranger(
  time,
  event,
  X,
  newdata,
  new.times,
  obsWeights,
  id,
  num.trees = 500,
  mtry = NULL,
  min.node.size = NULL,
  ...
)

Arguments

Value

A list containing:

• fit: The fitted model object (e.g., the raw coxph or xgb.Booster object). If the model fails to fit, this may be an object of class try-error.

• pred: A numeric matrix of cross-validated survival predictions evaluated at the specified new.times grid.

Examples

if (requireNamespace("ranger", quietly = TRUE)) {
  data("metabric", package = "SuperSurv")
  dat <- metabric[1:30, ]
  x_cols <- grep("^x", names(dat))[1:3]
  X <- dat[, x_cols, drop = FALSE]
  newX <- X[1:5, , drop = FALSE]
  times <- seq(50, 150, by = 50)

  fit <- surv.ranger(
    time = dat$duration,
    event = dat$event,
    X = X,
    newdata = newX,
    new.times = times,
    obsWeights = rep(1, nrow(dat)),
    id = NULL,
    num.trees = 10,
    min.node.size = 3
  )

  dim(fit$pred)
}

Wrapper function for Random Survival Forests (RFSRC)

Description

Final Production Wrapper for RFSRC (Tunable & Robust). Estimates a survival random forest using rfsrc.

Usage

surv.rfsrc(
  time,
  event,
  X,
  newdata = NULL,
  new.times,
  obsWeights = NULL,
  id = NULL,
  ntree = 1000,
  nodesize = 15,
  mtry = NULL,
  ...
)

Arguments

Value

A list containing:

• fit: The fitted model object (e.g., the raw coxph or xgb.Booster object). If the model fails to fit, this may be an object of class try-error.

• pred: A numeric matrix of cross-validated survival predictions evaluated at the specified new.times grid.

Examples

if (requireNamespace("randomForestSRC", quietly = TRUE)) {
  data("metabric", package = "SuperSurv")
  dat <- metabric[1:30, ]
  x_cols <- grep("^x", names(dat))[1:3]
  X <- dat[, x_cols, drop = FALSE]
  newX <- X[1:5, , drop = FALSE]
  times <- seq(50, 150, by = 50)

  fit <- surv.rfsrc(
    time = dat$duration,
    event = dat$event,
    X = X,
    newdata = newX,
    new.times = times,
    ntree = 10,
    nodesize = 3
  )

  dim(fit$pred)
}

Wrapper for Ridge Regression (Penalized Cox)

Description

Final Production Wrapper for Ridge Regression (Tunable & Robust). Estimates a penalized Cox model using a pure Ridge penalty (alpha = 0).

Usage

surv.ridge(
  time,
  event,
  X,
  newdata,
  new.times,
  obsWeights = NULL,
  id = NULL,
  nfolds = 10,
  ...
)

Arguments

Value

A list containing:

• fit: The fitted model object (e.g., the raw coxph or xgb.Booster object). If the model fails to fit, this may be an object of class try-error.

• pred: A numeric matrix of cross-validated survival predictions evaluated at the specified new.times grid.

Examples

if (requireNamespace("glmnet", quietly = TRUE)) {
  data("metabric", package = "SuperSurv")
  dat <- metabric[1:30, ]
  x_cols <- grep("^x", names(dat))[1:3]
  X <- dat[, x_cols, drop = FALSE]
  newX <- X[1:5, , drop = FALSE]
  times <- seq(50, 150, by = 50)

  fit <- surv.ridge(
    time = dat$duration,
    event = dat$event,
    X = X,
    newdata = newX,
    new.times = times,
    nfolds = 3
  )

  dim(fit$pred)
}

Wrapper for Survival Regression Trees (rpart)

Description

Final Production Wrapper for single decision trees. Uses rpart with method="exp" and calculates survival probabilities using the Breslow estimator.

Usage

surv.rpart(
  time,
  event,
  X,
  newdata,
  new.times,
  obsWeights,
  id,
  cp = 0.01,
  minsplit = 20,
  maxdepth = 30,
  ...
)

Arguments

Value

A list containing:

• fit: The fitted model object (e.g., the raw coxph or xgb.Booster object). If the model fails to fit, this may be an object of class try-error.

• pred: A numeric matrix of cross-validated survival predictions evaluated at the specified new.times grid.

Examples

if (requireNamespace("rpart", quietly = TRUE)) {
  data("metabric", package = "SuperSurv")
  dat <- metabric[1:30, ]
  x_cols <- grep("^x", names(dat))[1:3]
  X <- dat[, x_cols, drop = FALSE]
  newX <- X[1:5, , drop = FALSE]
  times <- seq(50, 150, by = 50)

  fit <- surv.rpart(
    time = dat$duration,
    event = dat$event,
    X = X,
    newdata = newX,
    new.times = times,
    obsWeights = rep(1, nrow(dat)),
    id = NULL,
    cp = 0.01,
    minsplit = 5,
    maxdepth = 3
  )

  dim(fit$pred)
}

Wrapper for Survival Support Vector Machine (survivalsvm)

Description

Final Production Wrapper for SVM (Tunable & Robust). Estimates a survival SVM and calibrates the raw utility scores into survival probabilities using a univariate Cox proportional hazards model.

Usage

surv.svm(
  time,
  event,
  X,
  newdata,
  new.times,
  obsWeights,
  id,
  gamma.mu = 0.1,
  type = "vanbelle2",
  kernel = "lin_kernel",
  opt.meth = "quadprog",
  ...
)

Arguments

Value

A list containing:

• fit: The fitted model object (e.g., the raw coxph or xgb.Booster object). If the model fails to fit, this may be an object of class try-error.

• pred: A numeric matrix of cross-validated survival predictions evaluated at the specified new.times grid.

Examples

if (requireNamespace("survivalsvm", quietly = TRUE) &&
 requireNamespace("quadprog", quietly = TRUE)) {
  data("metabric", package = "SuperSurv")
  dat <- metabric[1:25, ]
  x_cols <- grep("^x", names(dat))[1:3]
  X <- dat[, x_cols, drop = FALSE]
  newX <- X[1:5, , drop = FALSE]
  times <- seq(50, 150, by = 50)

  fit <- surv.svm(
    time = dat$duration,
    event = dat$event,
    X = X,
    newdata = newX,
    new.times = times
  )

  dim(fit$pred)
}

Parametric Survival Prediction Wrapper (Weibull)

Description

Parametric Survival Prediction Wrapper (Weibull)

Usage

surv.weibull(time, event, X, newdata, new.times, obsWeights, id, ...)

Arguments

Value

A list containing the fitted model and predictions.

Examples

data("metabric", package = "SuperSurv")
dat <- metabric[1:30, ]
x_cols <- grep("^x", names(dat))[1:3]
X <- dat[, x_cols, drop = FALSE]
newX <- X[1:5, , drop = FALSE]
times <- seq(50, 150, by = 50)

fit <- surv.weibull(
  time = dat$duration,
  event = dat$event,
  X = X,
  newdata = newX,
  new.times = times,
  obsWeights = rep(1, nrow(dat)),
  id = NULL
)

dim(fit$pred)

Wrapper for XGBoost (Robust CV-Tuned + Safe Prediction)

Description

Estimates a Cox proportional hazards model via XGBoost. Incorporates safe Breslow hazard calculation and matrix alignment to prevent C++ crashes.

Usage

surv.xgboost(
  time,
  event,
  X,
  newdata = NULL,
  new.times,
  obsWeights,
  id,
  nrounds = 1000,
  early_stopping_rounds = 10,
  eta = 0.05,
  max_depth = 2,
  min_child_weight = 5,
  lambda = 10,
  subsample = 0.7,
  ...
)

Arguments

Value

A list containing:

• fit: The fitted model object (e.g., the raw coxph or xgb.Booster object). If the model fails to fit, this may be an object of class try-error.

• pred: A numeric matrix of cross-validated survival predictions evaluated at the specified new.times grid.

Examples

if (requireNamespace("xgboost", quietly = TRUE)) {
  data("metabric", package = "SuperSurv")
  dat <- metabric[1:30, ]
  x_cols <- grep("^x", names(dat))[1:3]
  X <- dat[, x_cols, drop = FALSE]
  newX <- X[1:5, , drop = FALSE]
  times <- seq(50, 150, by = 50)

  fit <- surv.xgboost(
    time = dat$duration,
    event = dat$event,
    X = X,
    newdata = newX,
    new.times = times,
    obsWeights = rep(1, nrow(dat)),
    id = NULL,
    nrounds = 5,
    early_stopping_rounds = 2,
    max_depth = 1
  )

  dim(fit$pred)
}