Overview

Quick start

Are you comfortable writing Surv(time, status) ~ strata but hesitant to dive into the Fine-Gray models or custom ggplot2 code? cifmodeling helps clinicians, epidemiologists, and applied researchers move from basic Kaplan-Meier curves to clear, publication-ready survival and competing risk plots – with just a few lines of R.

It provides a unified, high-level interface for survival and competing risks analysis, combining nonparametric estimation, regression modeling, and visualization.

library(cifmodeling)
data(diabetes.complications)
cifplot(Event(t,epsilon)~fruitq, data=diabetes.complications, 
        outcome.type="competing-risk", panel.per.event=TRUE)

Aalen-Johansen cumulative incidence curves from cifplot()

In competing risks data, censoring is often coded as 0, the event of interest as 1, and competing risks as 2. In the diabetes.complications data frame, epsilon follows this convention. With panel.per.event = TRUE, cifplot() visualizes both competing events, with the CIF of diabetic retinopathy (epsilon = 1) shown on the left and the CIF of macrovascular complications (epsilon = 2) on the right.

Why cifmodeling?

In clinical and epidemiologic research, analysts often need to handle censoring, competing risks, and intercurrent events (e.g. treatment switching), but existing R packages typically separate these tasks across different interfaces. cifmodeling provides a unified, publication-ready toolkit that integrates nonparametric estimation, regression modeling, and visualization for survival and competing risks data. The tools assist users in the following ways:

• Unified interface for the Kaplan–Meier and Aalen–Johansen curves, with survival and competing risks handled by the same Event() + formula + data syntax.
• Effects on the CIF scale: while the Fine-Gray models subdistribution hazards, polyreg() directly targets ratios of CIFs (risk ratios, odds ratios, subdistribution hazard ratios), so parameters align closely with differences seen in CIF curves.
• Coherent, joint modeling of all competing events: polyreg() models all cause-specific CIFs together, parameterizing the nuisance structure with polytomous log odds products and enforcing that their CIFs sum to at most one.
• Tidy summaries and reporting: support for generics::tidy(), glance(), and augment(), which integrate polyreg() smoothly with modelsummary and other broom-style tools.
• Publication-ready graphics built on ggsurvfit and ggplot2, including number-at-risk/CIF+CI tables, censoring/competing-risk/intercurrent-event marks, and multi-panel layouts.

Tools for survival and competing risks analysis

The cifmodeling package is centered around three tightly connected functions:

• cifplot() generates a survival or cumulative incidence function (CIF) curve. The visualization is built on top of ggsurvfit and ggplot2.
• cifpanel() creates multi-panel displays for survival/CIF curves, arranged either in a grid layout or as an inset overlay.
• polyreg() fits coherent regression models on all cause-specific CIFs simultaneously to estimate RR/OR/SHR, offering a practical complement to Fine-Gray.

These functions adopt a formula + data syntax, return tidy, publication-ready outputs, and integrate seamlessly with ggsurvfit and modelsummary for visualization and reporting.

Explore the main features visually:
See the Gallery for a curated set of examples using cifplot() and cifpanel().

Interested in the precise variance formulas and influence functions for the Aalen-Johansen estimator?
Visit Computational formulas in cifcurve().

Learn the modeling framework with polyreg():
See the Direct polytomous regression for coherent, joint modeling of all cause-specific CIFs.

Prefer to build intuition before diving into code?
Visit the Coffee and Research – Story and Quiz series for narrative-style introductions to study design, survival and competing risks analysis, and frequentist and causality thinking: https://gestimation.github.io/coffee-and-research/en/

Position in the survival ecosystem

Several excellent R packages exist for survival and competing risks analysis. The survival package provides the canonical API for survival data. In combination with the ggsurvfit package, survival::survfit() can produce publication-ready survival plots. For CIF plots, however, integration in the general ecosystem is less streamlined. cifmodeling fills this gap by offering cifplot() for survival/CIF plots and multi-panel figures via a single, unified interface.

Beyond providing a unified interface, cifcurve() also extends survfit() in a few targeted ways. For unweighted survival data, it reproduces the standard Kaplan-Meier estimator with Greenwood and Tsiatis SEs and a unified set of CI transformations. For competing risks data, it computes Aalen-Johansen CIFs with both Aalen-type and delta-method SEs. For weighted survival or competing risks data (e.g. inverse probability weighting), it implements influence-function based SEs (Deng and Wang 2025) as well as modified Greenwood- and Tsiatis-type SEs (Xie and Liu 2005), which are valid under general positive weights.

If you need very fine-grained plot customization, you can compute the estimator and keep a survfit-compatible object with cifcurve() (or supply your own survfit object) and then style it using ggsurvfit/ggplot2 layers. In other words:

• use cifcurve() for estimation,
• use cifplot() / cifpanel() for quick, high-quality figures, and
• fall back to the ggplot ecosystem when you want full artistic control.

The causalRisk package offers IPW-based estimation of counterfactual cumulative risks and hazards. It is most relevant when treatment, censoring, and missingness mechanisms must be modeled explicitly. In contrast, cifmodeling focuses on nonparametric estimation and direct CIF regression rather than structural causal models.

The mets package is a more specialized toolkit that provides advanced methods for competing risks analysis. cifmodeling::polyreg() focuses on coherent modeling of all CIFs simultaneously to estimate the exposure effects in terms of RR/OR/SHR. This coherence can come with longer runtimes for large problems. If you prefer fitting separate regression models for each competing event or specifically need the Fine-Gray models (Fine and Gray 1999) and the direct binomial models (Scheike, Zhang and Gerds 2008), mets::cifreg() and mets::binreg() are excellent choices.

In short, cifmodeling provides a unified high-level grammar for estimation, visualization, and direct CIF regression — something no existing package currently offers in one place.

Installation

The package is implemented in R and relies on Rcpp, nleqslv and boot for its numerical back-end. The examples in this document also use ggplot2, ggsurvfit, patchwork and modelsummary for tabulation and plotting. Install the core package and these companion packages with:

# Install cifmodeling with core dependencies
install.packages(c("cifmodeling", "Rcpp", "nleqslv", "boot"))

# Recommended packages for plotting and tabulation in this README
install.packages(c("ggplot2", "ggsurvfit", "patchwork", "modelsummary"))

Quality control

cifmodeling includes an extensive test suite built with testthat, which checks the numerical accuracy and graphical consistency of all core functions (cifcurve(), cifplot(), cifpanel(), and polyreg()). The estimators are routinely compared against related functions in survival, cmprsk and mets packages to ensure consistency. The package is continuously tested on GitHub Actions (Windows, macOS, Linux) to maintain reproducibility and CRAN-level compliance.