casebase: 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.

Version: 0.1.0
Depends: R (≥ 3.3.1)
Imports: data.table, ggplot2, methods, survival, VGAM
Suggests: eha, knitr, rmarkdown, splines, testthat
Published: 2017-04-28
Author: Sahir Bhatnagar [aut, cre] (, Maxime Turgeon [aut] (, Olli Saarela [aut] (, James Hanley [aut] (
Maintainer: Sahir Bhatnagar <sahir.bhatnagar at>
License: MIT + file LICENSE
NeedsCompilation: no
Citation: casebase citation info
Materials: README NEWS
In views: Survival
CRAN checks: casebase results


Reference manual: casebase.pdf
Vignettes: Competing risk analysis
Population Time Plots
Introduction to casebase sampling
Package source: casebase_0.1.0.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
OS X El Capitan binaries: r-release: casebase_0.1.0.tgz
OS X Mavericks binaries: r-oldrel: casebase_0.1.0.tgz


Please use the canonical form to link to this page.