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

Fit flexible and fully parametric hazard regression models to survival data with single event type or multiple competing causes via logistic and multinomial regression. Our formulation allows for arbitrary functional forms of time and its interactions with other predictors for time-dependent hazards and hazard ratios. From the fitted hazard model, we provide functions to readily calculate and plot cumulative incidence and survival curves for a given covariate profile. 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. Based on the case-base sampling approach of Hanley and Miettinen (2009) , Saarela and Arjas (2015) , and Saarela (2015) .

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

• Issues: https://github.com/sahirbhatnagar/casebase/issues
• Pull Requests: https://github.com/sahirbhatnagar/casebase/
• e-mail: [email protected], [email protected]

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.