Benchmarking and Visualization Toolkit for Penalized Cox Models

Creates nomogram visualizations for penalized Cox regression models, with the support of reproducible survival model building, validation, calibration, and comparison for high-dimensional data.


hdnom builds nomograms for high-dimensional data with penalized Cox models.

Formatted citation for the preprint:

Nan Xiao, Qing-Song Xu, and Miao-Zhu Li. "hdnom: Building Nomograms for Penalized Cox Models with High-Dimensional Survival Data." bioRxiv (2016): 065524; doi: http://dx.doi.org/10.1101/065524

BibTeX entry:

@article {hdnompreprint2016,
    author = {Xiao, Nan and Xu, Qing-Song and Li, Miao-Zhu},
    title = {hdnom: Building Nomograms for Penalized Cox Models with High-Dimensional Survival Data},
    year = {2016},
    doi = {10.1101/065524},
    publisher = {Cold Spring Harbor Labs Journals},
    URL = {http://biorxiv.org/content/early/2016/08/23/065524},
    eprint = {http://biorxiv.org/content/early/2016/08/23/065524.full.pdf},
    journal = {bioRxiv}
}

To download and install hdnom from CRAN:

install.packages("hdnom")

Or try the development version on GitHub:

# install.packages("devtools")
devtools::install_github("road2stat/hdnom")

To load the package in R, simply use

library("hdnom")

and you are all set. See the vignette (can also be opened with vignette("hdnom") in R) for a quick-start guide.

News

hdnom 4.5 (2016-12-24)

  • Fixed vanishing axis problem in Kaplan-Meier plot hdnom.kmplot() under ggplot2 2.2.0, which is caused by a previous workaround for a bug introduced in ggplot2 2.1.0.
  • Fixed potential convergence issues for examples under ncvreg >= 3.7-0 new convergence criterion, by increasing max.iter for ncvsurv to a substantially higher value (5e+4).
  • Fixed single lambda support issues in ncvsurv under ncvreg >= 3.7-0.
  • Added Windows continuous integration using AppVeyor
  • New website design for hdnom.org: consistent with the web application hdnom.io.

hdnom 4.0 (2016-07-05)

  • More concrete examples for several functions
  • Introduce argument ylim for plot.hdnom.validate(), plot.hdnom.external.validate(), and plot.hdnom.compare.validate() #4.

hdnom 3.7 (2016-03-25)

  • Removed one redundant color from the lancet color palette

hdnom 3.6 (2016-03-24)

  • Added 4 new color palettes (JCO, Lancet, NPG, AAAS) for all plots. See the vignette for details.
  • Fixed vanishing axis problem in Kaplan-Meier plot due to ggplot2 2.1.0 update
  • New CSS style for the HTML vignette

hdnom 3.0 (2016-01-03)

  • New function hdnom.compare.validate() for model comparison by validation
  • New function hdnom.compare.calibrate() for model comparison by calibration
  • New function hdnom.external.validate() for external validation
  • New function hdnom.external.calibrate() for external calibration
  • New predict and print methods for hdcox.model objects
  • New function hdnom.kmplot(): Kaplan-Meier analysis for risk groups using internal/external calibration results
  • New function hdnom.logrank(): Log-rank test for risk groups using internal/external calibration results
  • The web application is substantially improved to reflect the new features
  • Record random seeds in the generated reports to improve reproducibility
  • Allow users to download the model objects for further exploration in R
  • Improvements on random seed handling for internal validation and calibration
  • The vignette is extended to reflect the new features
  • Fixed an error in internal calibration which mistakenly used testing data when should use training data in computation

hdnom 2.1 (2015-10-26)

  • New website: http://hdnom.org
  • Shiny-based web application (http://hdnom.io) shipped.
  • Added exception handling for null models in all hdcox.*() functions. Make examples compatible with ncvreg 3.5-0, which refined CV implementation for survival models and improved computation speed.

hdnom 2.0 (2015-09-15)

  • Support five more high-dimensional penalized Cox model types:

    • Fused lasso
    • MCP
    • Mnet
    • SCAD
    • Snet

hdnom 1.2 (2015-08-27)

  • Reduced example running time for hdnom.validate(), hdnom.calibrate(), hdcox.aenet(), and hdcox.enet() by reducing resampling times.

hdnom 1.1 (2015-08-27)

  • Added argument parallel to hdcox.aenet() and hdcox.enet() to enable or disable the use of parallel parameter tuning.

hdnom 1.0 (2015-08-04)

  • Initial version
  • Nomograms for glmnet models
  • Validation for glmnet models
  • Calibration for glmnet models

Reference manual

It appears you don't have a PDF plugin for this browser. You can click here to download the reference manual.

install.packages("hdnom")

4.9 by Nan Xiao, 3 months ago


https://hdnom.org, https://github.com/road2stat/hdnom, http://hdnom.io


Report a bug at https://github.com/road2stat/hdnom/issues


Browse source code at https://github.com/cran/hdnom


Authors: Nan Xiao [aut, cre], Qingsong Xu [aut], Miaozhu Li [aut]


Documentation:   PDF Manual  


Task views: Survival Analysis


GPL-3 | file LICENSE license


Imports survival, glmnet, penalized, ncvreg, rms, foreach, survAUC, ggplot2, gridExtra

Suggests knitr, rmarkdown, doParallel, Hmisc, mice


See at CRAN