OBJECTIVE: Clinical prediction models providing binary categorizations for clinical decision support require the selection of a probability threshold, or "cutpoint," to classify individuals. Existing cutpoint selection approaches typically optimize test-specific metrics, including sensitivity and specificity, but overlook the consequences of correct or incorrect classification. We introduce a new cutpoint selection approach considering downstream consequences using net monetary benefit (NMB) and through simulations compared it with alternative approaches in 2 use-cases: (i) preventing intensive care unit readmission and (ii) preventing inpatient falls. MATERIALS AND METHODS: Parameter estimates for costs and effectiveness from prior studies were included in Monte Carlo simulations. For each use-case, we simulated the expected NMB resulting from the model-guided decision using a range of cutpoint selection approaches, including our new value-optimizing approach. Sensitivity analyses applied alternative event rates, model discrimination, and calibration performance. RESULTS: The proposed approach that considered expected downstream consequences was frequently NMB-maximizing compared with other methods. Sensitivity analysis demonstrated that it was or closely tracked the optimal strategy under a range of scenarios. Under scenarios of relatively low event rates and discrimination that may be considered realistic for intensive care (prevalence = 0.025, area under the receiver operating characteristic curve [AUC] = 0.70) and falls (prevalence = 0.036, AUC = 0.70), our proposed cutpoint method was either the best or similar to the best of the compared methods regarding NMB, and was robust to model miscalibration. DISCUSSION: Our results highlight the potential value of conditioning cutpoints on the implementation setting, particularly for rare and costly events, which are often the target of prediction model development research. CONCLUSIONS: This study proposes a cutpoint selection method that may optimize clinical decision support systems toward value-based care.

MOTIVATION: A fundamental interest in chronobiology is to compare patterns between groups of rhythmic data. However, many existing methods are ill-equipped to derive statements concerning the statistical significance of differences between rhythms that may be visually apparent. This is attributed to both the form of data used (longitudinal versus cross-sectional) and the limitations of the statistical tests used to draw conclusions. RESULTS: To address this problem, we propose that a cosinusoidal curve with a particular parametrization be used to model and compare data of two sets of observations collected over a 24-h period. The novelty of our test is in the parametrization, which allows the explicit estimation of rhythmic parameters [mesor (the rhythm-adjusted mean level of a response variable around which a wave function oscillates), amplitude and phase], and simultaneously testing for statistical significance in all three parameters between two or more groups of datasets. A statistically significant difference between two groups, regarding each of these rhythmic parameters, is indicated by a P-value. The method is evaluated by applying the model to publicly available datasets, and is further exemplified by comparison to the currently recommended method, DODR. The results suggest that the method proposed may be highly sensitive to detect rhythmic differences between groups in phase, amplitude and mesor. AVAILABILITY AND IMPLEMENTATION: https://github.com/RWParsons/circacompare/.