contsurvplot: Visualize the Effect of a Continuous Variable on a Time-to-Event
Outcome
Graphically display the (causal) effect of a continuous variable on a time-to-event outcome
using multiple different types of plots based on g-computation. Those functions
include, among others, survival area plots, survival contour plots, survival quantile plots and
3D surface plots. Due to the use of g-computation, all plot allow confounder-adjustment naturally.
For details, see Robin Denz, Nina Timmesfeld (2023) <doi:10.1097/EDE.0000000000001630>.
| Version: |
0.2.1 |
| Imports: |
ggplot2 (≥ 3.4.0), dplyr, rlang, riskRegression, foreach |
| Suggests: |
survival, pammtools, gganimate, transformr, plotly, reshape2, doParallel, knitr, rmarkdown, testthat (≥ 3.0.0), vdiffr (≥
1.0.0), covr |
| Published: |
2023-08-15 |
| DOI: |
10.32614/CRAN.package.contsurvplot |
| Author: |
Robin Denz [aut, cre] |
| Maintainer: |
Robin Denz <robin.denz at rub.de> |
| Contact: |
<robin.denz@rub.de> |
| BugReports: |
https://github.com/RobinDenz1/contsurvplot/issues |
| License: |
GPL (≥ 3) |
| URL: |
https://github.com/RobinDenz1/contsurvplot,
https://robindenz1.github.io/contsurvplot/ |
| NeedsCompilation: |
no |
| Citation: |
contsurvplot citation info |
| Materials: |
README NEWS |
| CRAN checks: |
contsurvplot results |
Documentation:
Downloads:
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