# casebase: An Alternative Framework For Survival Analysis and Comparison of Event Rates

arXiv: https://arxiv.org/abs/2009.10264 · HTML: https://mlsanity.com/paper/2009.10264/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Sep 2020.

## Built on by (most cited first, 1 of 1)

- [Case-Base Neural Networks: survival analysis with time-varying, higher-order interactions](https://mlsanity.com/paper/2301.06535/) (2023)

## Abstract (arXiv)

In epidemiological studies of time-to-event data, a quantity of interest to the clinician and the patient is the risk of an event given a covariate profile. However, methods relying on time matching or risk-set sampling (including Cox regression) eliminate the baseline hazard from the likelihood expression or the estimating function. The baseline hazard then needs to be estimated separately using a non-parametric approach. This leads to step-wise estimates of the cumulative incidence that are difficult to interpret. Using case-base sampling, Hanley & Miettinen (2009) explained how the parametric hazard functions can be estimated using logistic regression. Their approach naturally leads to estimates of the cumulative incidence that are smooth-in-time. In this paper, we present the casebase R package, a comprehensive and flexible toolkit for parametric survival analysis. We describe how the case-base framework can also be used in more complex settings: competing risks, time-varying exposure, and variable selection. Our package also includes an extensive array of visualization tools to complement the analysis of time-to-event data. We illustrate all these features through four different case studies. *SRB and MT contributed equally to this work.