Function reference • hdnom

Building Cox models

Functions for building penalized Cox models.

fit_lasso(): Model selection for high-dimensional Cox models with lasso penalty

fit_alasso(): Model selection for high-dimensional Cox models with adaptive lasso penalty

fit_enet(): Model selection for high-dimensional Cox models with elastic-net penalty

fit_aenet(): Model selection for high-dimensional Cox models with adaptive elastic-net penalty

fit_scad(): Model selection for high-dimensional Cox models with SCAD penalty

fit_snet(): Model selection for high-dimensional Cox models with Snet penalty

fit_mcp(): Model selection for high-dimensional Cox models with MCP penalty

fit_mnet(): Model selection for high-dimensional Cox models with Mnet penalty

fit_flasso(): Model selection for high-dimensional Cox models with fused lasso penalty

print(<hdnom.model>): Print high-dimensional Cox model objects

predict(<hdnom.model>): Make predictions from high-dimensional Cox models

infer_variable_type(): Extract information of selected variables from high-dimensional Cox models

Nomogram visualization

Functions for nomogram visualization of the penalized Cox models

as_nomogram(): Construct nomogram ojects for high-dimensional Cox models

print(<hdnom.nomogram>): Print nomograms objects

plot(<hdnom.nomogram>): Plot nomogram objects

Model validation

Functions for model validation using bootstrap, k-fold cross-validation, and repeated k-fold cross-validation. Model performance is assessed by time-dependent AUC (tAUC).

validate(): Validate high-dimensional Cox models with time-dependent AUC

print(<hdnom.validate>): Print validation results

summary(<hdnom.validate>): Summary of validation results

plot(<hdnom.validate>): Plot optimism-corrected time-dependent discrimination curves for validation

validate_external(): Externally validate high-dimensional Cox models with time-dependent AUC

print(<hdnom.validate.external>): Print external validation results

summary(<hdnom.validate.external>): Summary of external validation results

plot(<hdnom.validate.external>): Plot time-dependent discrimination curves for external validation

Model calibration

Functions for model calibration using direct fitting, bootstrap resampling, k-fold cross-validation, and repeated cross-validation.

calibrate(): Calibrate high-dimensional Cox models

print(<hdnom.calibrate>): Print calibration results

summary(<hdnom.calibrate>): Summary of calibration results

plot(<hdnom.calibrate>): Plot calibration results

calibrate_external(): Externally calibrate high-dimensional Cox models

print(<hdnom.calibrate.external>): Print external calibration results

summary(<hdnom.calibrate.external>): Summary of external calibration results

plot(<hdnom.calibrate.external>): Plot external calibration results

kmplot(): Kaplan-Meier plot with number at risk table for internal calibration and external calibration results

logrank_test(): Log-rank test for internal calibration and external calibration results

Model comparison

Functions for model comparison in terms of validation and calibration performance.

compare_by_validate(): Compare high-dimensional Cox models by model validation

print(<hdnom.compare.validate>): Print model comparison by validation results

summary(<hdnom.compare.validate>): Summary of model comparison by validation results

plot(<hdnom.compare.validate>): Plot model comparison by validation results

compare_by_calibrate(): Compare high-dimensional Cox models by model calibration

print(<hdnom.compare.calibrate>): Print model comparison by calibration results

summary(<hdnom.compare.calibrate>): Summary of model comparison by calibration results

plot(<hdnom.compare.calibrate>): Plot model comparison by calibration results

Miscellaneous

Miscellaneous functions for supporting survival analysis, such as baseline hazard estimation and survival curve prediction.

theme_hdnom(): Plot theme (ggplot2) for hdnom

glmnet_basesurv(): Breslow baseline hazard estimator for glmnet objects

glmnet_survcurve(): Survival curve prediction for glmnet objects

ncvreg_basesurv(): Breslow baseline hazard estimator for ncvreg objects

ncvreg_survcurve(): Survival curve prediction for ncvreg objects

penalized_basesurv(): Breslow baseline hazard estimator for penfit objects

penalized_survcurve(): Survival curve prediction for penfit objects

hdnom hdnom-package: hdnom: Benchmarking and Visualization Toolkit for Penalized Cox Models

Datasets

Example datasets

smart: Imputed SMART study data

smarto: Original SMART study data