Recent criteria and simple rules agreed often on required sample size for developed clinical prediction models.

OBJECTIVE: Adequate sample size is essential to the development of new prediction models with binary outcomes. We aim to relate recent approaches to traditional rules of thumb ('simple rules') and assess agreement and differences in judging adequacy of sample size for developed prediction models. STUDY DESIGN AND SETTING: A well-known simple rule considers the events per variable (EPV), or more specifically, events per predictor parameter p (EPP, e.g. 'EPP>10', or 'EPP>20') to limit overfitting to small data sets. Another simple rule is to require a minimum absolute number of events (E) for reliable estimation of the overall event rate (e.g. 'E>100'). Recent criteria for regression-based prediction models consider: 1) limited overfitting in predictor effect estimates ('global shrinkage ≥ 0.9'); 2) small optimism in Nagelkerke's R2 ('δ(R2) ≤ 0.05'); and 3) precise estimation of the overall event rate (margin of error <0.05). We use statistical theory to compare simple rules to these three recent criteria. Furthermore, we compare sample size assessments for 299 published COVID-19 prediction models. RESULTS: At a 10% event rate, criterion 1 (limited overfitting) corresponded to the classic EPP>10 rule for an Area under the ROC curve (c) of 0.767, and EPP>20 for c= 0.694. Criterion 2 implied EPP>4 (largely irrespective of c) and criterion 3 was equivalent to E>14 at a 10% event rate. We classified largely the same COVID-19 prediction models as adequate or inadequate for sample size according to recent criteria (maximum of three) and a simple combination rule ('E=100 plus 10*p', or max('E=100, 10*p)). CONCLUSION: Simple rules have direct relations with recent criteria for sample size calculations to limit overfitting and to guarantee reliability of predictions. Recent criteria are essential to inform prediction modeling efforts, while simple rules may often be sufficient to help appraise the quality of already developed regression-based prediction models.