Ch 4: Statistical models for prediction

Clinical decision-making often relies on a subject’s absolute risk of a disease event of interest. However, in a frail population, competing risk events may preclude the occurrence of the event of interest. Competing-risk regression models can hence be useful. The Fine and Gray model is popular. Cause-specific model are also quite attractive, e.g. because it does not provide event specific probabilities that add up to more than 1. We applied competing risks methods to coronary risk prediction (Epidemiology, July 2009). An excellent tutorial was published by Hein Putter et al in 2007. Fine and Gray regression models can be fitted with the contributed R package cmprsk and the function FGR in the R package riskRegression provides a convenient formula interface to fit these models.

An IPCW-estimator for the concordance probability has recently been proposed and implemented in the R-function cindex in the R package pec. Some additional R functions associated with our paper on coronary risk prediction (Epidemiology, July 2009) and programmed by Marcel Wolbers are available but are now largely superseded by the above-mentioned packages.

A framework for evaluation of competing risk models was published in 2022, led by Nan van Geloven as part of the STRATOS initiative. Key performance measures are illustrated with a case study in breast cancer; R code is here.