A classic endometrial cancer dataset used to demonstrate separation in logistic regression. The outcome is high histology grade. Neovascularization is completely absent among low-grade cases in this dataset, making it useful for teaching Firth penalized logistic regression.
Format
A data frame with 79 observations and 4 variables:
- NV
Neovascularization status (0 = absent, 1 = present)
- PI
Pulsatility index of the uterine artery
- EH
Endometrium height
- HG
Histology grade (0 = low grade, 1 = high grade)
Source
brglm2 package. The packaged dataset was sourced from https://users.stat.ufl.edu/~aa/glm/data/, the data repository used in Agresti (2015). Originally analyzed in Heinze and Schemper (2002).
References
Agresti A (2015). Foundations of Linear and Generalized Linear Models. Wiley.
Heinze G, Schemper M (2002). A solution to the problem of separation in logistic regression. Statistics in Medicine, 21, 2409-2419. doi:10.1002/sim.1047