casebase v0.1.0

0

Monthly downloads

0th

Percentile

Fitting Flexible Smooth-in-Time Hazards and Risk Functions via Logistic and Multinomial Regression

Implements the case-base sampling approach of Hanley and Miettinen (2009) <DOI:10.2202/1557-4679.1125>, Saarela and Arjas (2015) <DOI:10.1111/sjos.12125>, and Saarela (2015) <DOI:10.1007/s10985-015-9352-x>, for fitting flexible hazard regression models to survival data with single event type or multiple competing causes via logistic and multinomial regression. From the fitted hazard function, cumulative incidence, risk functions of time, treatment and profile can be derived. This approach accommodates any log-linear hazard function of prognostic time, treatment, and covariates, and readily allows for non-proportionality. We also provide a plot method for visualizing incidence density via population time plots.

Readme

casebase

Build Status Coverage Status

An R package for smooth-in-time fitting of parametric hazard functions

Installation

You can install the development version of casebase from GitHub with:

install.packages("pacman")
pacman::p_install_gh("sahirbhatnagar/casebase")

Vignette

See the package website for example usage of the functions. This includes

  1. Fitting Smooth Hazard Functions
  2. Competing Risks Analysis
  3. Population Time Plots

Credit

This package is makes use of several existing packages including:

  • VGAM for fitting multinomial logistic regression models
  • survival for survival models
  • ggplot2 for plotting the population time plots

Citation

To cite casebase in publications, please use

citation('casebase')
Bhatnagar S, Turgeon M, Saarela O and Hanley J (2017). 
casebase: Fitting Flexible Smooth-in-Time Hazards and Risk Functions via Logistic and Multinomial Regression. 
R package version 0.1.0, <URL:https://CRAN.R-project.org/package=casebase>.

Hanley, James A., and Olli S. Miettinen. 
Fitting smooth-in-time prognostic risk functions via logistic regression. 
International Journal of Biostatistics 5.1 (2009): 1125-1125.

Saarela, Olli. A case-base sampling method for estimating recurrent event intensities. 
Lifetime data analysis 22.4 (2016): 589-605.

If competing risks analyis is used, please also cite:

Saarela, Olli, and Elja Arjas. Non-parametric Bayesian Hazard Regression for Chronic Disease Risk Assessment. 
Scandinavian Journal of Statistics 42.2 (2015): 609-626.

For BibTeX users:

toBibtex(citation('casebase'))
@Manual{casebase-package,
  title = {casebase: Fitting Flexible Smooth-in-Time Hazards and Risk Functions via Logistic and Multinomial Regression},
  author = {Sahir Bhatnagar and Maxime Turgeon and Olli Saarela and James Hanley},
  year = {2017},
  note = {R package version 0.1.0},
  url = {https://CRAN.R-project.org/package=casebase},
}

@Article{,
  title = {Fitting smooth-in-time prognostic risk functions via logistic regression},
  author = {James A Hanley and Olli S Miettinen},
  journal = {International Journal of Biostatistics},
  volume = {5},
  number = {1},
  pages = {1125--1125},
  year = {2009},
  publisher = {Berkeley Electronic Press},
}

@Article{,
  title = {A case-base sampling method for estimating recurrent event intensities},
  author = {Olli Saarela},
  journal = {Lifetime data analysis},
  volume = {22},
  number = {4},
  pages = {589--605},
  year = {2016},
  publisher = {Springer},
}

@Article{,
  title = {Non-parametric Bayesian Hazard Regression for Chronic Disease Risk Assessment},
  author = {Olli Saarela and Elja Arjas},
  journal = {Scandinavian Journal of Statistics},
  year = {2015},
  volume = {42},
  number = {2},
  pages = {609--626},
  publisher = {Wiley Online Library},
}

References

  1. Hanley, James A, and Olli S Miettinen. 2009. "Fitting Smooth-in-Time Prognostic Risk Functions via Logistic Regression." The International Journal of Biostatistics 5 (1).

  2. Saarela, Olli, and Elja Arjas. 2015. "Non-Parametric Bayesian Hazard Regression for Chronic Disease Risk Assessment." Scandinavian Journal of Statistics 42 (2). Wiley Online Library: 609–26.

  3. Saarela, Olli. 2015. "A Case-Base Sampling Method for Estimating Recurrent Event Intensities." Lifetime Data Analysis. Springer, 1–17.

Contact

Latest news

You can see the most recent changes to the package in the NEWS.md file

Code of Conduct

Please note that this project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms.

Functions in casebase

Name Description
CompRisk-class An S4 class to store the output of fitSmoothHazard
ERSPC Data on the men in the European Randomized Study of Prostate Cancer Screening
plot.popTime Population Time Plot
sampleCaseBase Create case-base dataset for use in fitting parametric hazard functions
absoluteRisk Compute absolute risks using the fitted hazard function.
bmtcrr Data on transplant patients
checkArgsEventIndicator Check that Event is in Correct Format
fitSmoothHazard Fit smooth-in-time parametric hazard functions.
No Results!

Vignettes of casebase

Name
competingRisk.Rmd
popTime.Rmd
reference.bib
smoothHazard.Rmd
No Results!

Last month downloads

Details

Type Package
Date 2017-4-28
License MIT + file LICENSE
LazyData TRUE
VignetteBuilder knitr
URL http://sahirbhatnagar.com/casebase/
BugReports https://github.com/sahirbhatnagar/casebase/issues
RoxygenNote 6.0.1
NeedsCompilation no
Packaged 2017-04-28 19:29:44 UTC; sahir
Repository CRAN
Date/Publication 2017-04-28 23:27:54 UTC

Include our badge in your README

[![Rdoc](http://www.rdocumentation.org/badges/version/casebase)](http://www.rdocumentation.org/packages/casebase)