Objectives: Receiver operating characteristic (ROC) curves show how well a risk prediction model discriminates between patients with and without a condition. We aim to investigate how ROC curves... Show moreObjectives: Receiver operating characteristic (ROC) curves show how well a risk prediction model discriminates between patients with and without a condition. We aim to investigate how ROC curves are presented in the literature and discuss and illustrate their potential limitations.Study Design and Setting: We conducted a pragmatic literature review of contemporary publications that externally validated clinical prediction models. We illustrated limitations of ROC curves using a testicular cancer case study and simulated data.Results: Of 86 identified prediction modeling studies, 52 (60%) presented ROC curves without thresholds and one (1%) presented an ROC curve with only a few thresholds. We illustrate that ROC curves in their standard form withhold threshold information have an unstable shape even for the same area under the curve (AUC) and are problematic for comparing model performance conditional on threshold. We compare ROC curves with classification plots, which show sensitivity and specificity conditional on risk thresholds.Conclusion: ROC curves do not offer more information than the AUC to indicate discriminative ability. To assess the model's performance for decision-making, results should be provided conditional on risk thresholds. Therefore, if discriminatory ability must be visualized, classification plots are attractive. (C) 2020 Published by Elsevier Inc. Show less
Calster, B. van; Smeden, M. van; Cock, B. de; Steyerberg, E.W. 2020
When developing risk prediction models on datasets with limited sample size, shrinkage methods are recommended. Earlier studies showed that shrinkage results in better predictive performance on... Show moreWhen developing risk prediction models on datasets with limited sample size, shrinkage methods are recommended. Earlier studies showed that shrinkage results in better predictive performance on average. This simulation study aimed to investigate the variability of regression shrinkage on predictive performance for a binary outcome. We compared standard maximum likelihood with the following shrinkage methods: uniform shrinkage (likelihood-based and bootstrap-based), penalized maximum likelihood (ridge) methods, LASSO logistic regression, adaptive LASSO, and Firth's correction. In the simulation study, we varied the number of predictors and their strength, the correlation between predictors, the event rate of the outcome, and the events per variable. In terms of results, we focused on the calibration slope. The slope indicates whether risk predictions are too extreme (slope < 1) or not extreme enough (slope > 1). The results can be summarized into three main findings. First, shrinkage improved calibration slopes on average. Second, the between-sample variability of calibration slopes was often increased relative to maximum likelihood. In contrast to other shrinkage approaches, Firth's correction had a small shrinkage effect but showed low variability. Third, the correlation between the estimated shrinkage and the optimal shrinkage to remove overfitting was typically negative, with Firth's correction as the exception. We conclude that, despite improved performance on average, shrinkage often worked poorly in individual datasets, in particular when it was most needed. The results imply that shrinkage methods do not solve problems associated with small sample size or low number of events per variable. Show less
