Visualization and Polytomous Modeling of Survival and Competing Risks
These arguments are shared by cifplot(), cifpanel(), and `cifcurve...
These arguments are shared by cifplot() and cifpanel().
Calculate the Kaplan-Meier estimator and the Aalen-Johansen estimator
cifmodeling: Visualization and Polytomous Modeling of Survival and Com...
Arrange multiple survival and CIF plots in a panel display
Generate a survival/CIF curve with marks that represent censoring, com...
Create a survival or competing-risks response
Extract per-stratum event times from a formula and data
Methods for polyreg objects
Fit coherent regression models of CIFs using polytomous log odds produ...
A publication-ready toolkit for modern survival and competing risks analysis with a minimal, formula-based interface. Both nonparametric estimation and direct polytomous regression of cumulative incidence functions (CIFs) are supported. The main functions 'cifcurve()', 'cifplot()', and 'cifpanel()' estimate survival and CIF curves and produce high-quality graphics with risk tables, censoring and competing-risk marks, and multi-panel or inset layouts built on 'ggplot2' and 'ggsurvfit'. The modeling function 'polyreg()' performs direct polytomous regression for coherent joint modeling of all cause-specific CIFs to estimate risk ratios, odds ratios, or subdistribution hazard ratios at user-specified time points. All core functions adopt a formula-and-data syntax and return tidy and extensible outputs that integrate smoothly with 'modelsummary', 'broom', and the broader 'tidyverse' ecosystem. Key numerical routines are implemented in C++ via 'Rcpp'.
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