Explained variation measures the relative gain in predictive accuracy when pre- diction based on prognostic factors replaces unconditional prediction. The fac- tors may be measured on different scales or may be of different types (dichotomous, qualitative, or continuous). Thus, explained variation permits to establish a ranking of the importance of factors, even if predictive accuracy is too low to be helpful in clinical practice. In this contribution, the explained variation measure by Schemper and Henderson (2000) is extended to accommo- date random factors, such as center effects in multicenter studies. This permits a direct comparison of the importance of centers and of other prognostic fac- tors. We develop this extension for a shared frailty Cox model and provide an SAS macro and an R function to facilitate its application.
Gleiss A, Gnant M, Schemper M. Explained variation in shared frailty models. Statistics in Medicine. 2018;37:1482–1490. https://doi.org/10.1002/sim.7592