eventglm: Regression Models for Event History Outcomes

A user friendly, easy to understand way of doing event history regression for marginal estimands of interest, including the cumulative incidence and the restricted mean survival, using the pseudo observation framework for estimation. For a review of the methodology, see Andersen and Pohar Perme (2010) <doi:10.1177/0962280209105020>. The interface uses the well known formulation of a generalized linear model and allows for features including plotting of residuals, the use of sampling weights, and corrected variance estimation.

Version: 1.0.2
Depends: R (≥ 2.10)
Imports: survival, sandwich, stats
Suggests: testthat, prodlim, knitr, rmarkdown, rio, data.table
Published: 2020-11-10
Author: Michael C Sachs [aut, cre], Erin E Gabriel [aut], Morten Overgaard [ctb] (Corrected variance calculation), Thomas A Gerds [ctb] (Fast computation of leave one out cumulative incidence), Terry Therneau [ctb] (Restricted mean computation)
Maintainer: Michael C Sachs <sachsmc at gmail.com>
BugReports: https://github.com/sachsmc/eventglm/issues/
License: GPL-3
URL: https://sachsmc.github.io/eventglm/
NeedsCompilation: yes
Materials: README NEWS
CRAN checks: eventglm results


Reference manual: eventglm.pdf
Vignettes: Examples of using eventglm and interpreting the results
Package source: eventglm_1.0.2.tar.gz
Windows binaries: r-devel: eventglm_1.0.2.zip, r-release: eventglm_1.0.2.zip, r-oldrel: eventglm_1.0.2.zip
macOS binaries: r-release: eventglm_1.0.2.tgz, r-oldrel: eventglm_1.0.2.tgz
Old sources: eventglm archive


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