Risk prediction models for discrete ordinal outcomes: Calibration and the impact of the proportional odds assumption.

ABSTRACT: Calibration is a vital aspect of the performance of risk prediction models, but research in the context of ordinal outcomes is scarce. This study compared calibration measures for risk models predicting a discrete ordinal outcome, and investigated the impact of the proportional odds assumption on calibration and overfitting. We studied the multinomial, cumulative, adjacent category, continuation ratio, and stereotype logit/logistic models. To assess calibration, we investigated calibration intercepts and slopes, calibration plots, and the estimated calibration index. Using large sample simulations, we studied the performance of models for risk estimation under various conditions, assuming that the true model has either a multinomial logistic form or a cumulative logit proportional odds for

SUBMITTER: Edlinger M 

PROVIDER: S-EPMC9299669 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature