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Objective: To assess whether the outcome generation true model could be identified from other candidate models for clinical practice with current conventional model performance measures considering various simulation scenarios and a CVD risk prediction as exemplar. Study design and setting: Thousands of scenarios of true models were used to simulate clinical data, various candidate models and true models were trained on training datasets and then compared on testing datasets with 25 conventional use model performance measures. This consists of univariate simulation (179.2k simulated datasets and over 1.792 million models), multivariate simulation (728k simulated datasets and over 8.736 million models) and a CVD risk prediction case analysis. Results: True models had overall C statistic and 95% range of 0.67 (0.51, 0.96) across all scenarios in univariate simulation, 0.81 (0.54, 0.98) in multivariate simulation, 0.85 (0.82, 0.88) in univariate case analysis and 0.85 (0.82, 0.88) in multivariate case analysis. Measures showed very clear differences between the true model and flip-coin model, little or none differences between the true model and candidate models with extra noises, relatively small differences between the true model and proxy models missing causal predictors. Conclusion: The study found the true model is not always identified as the "outperformed" model by current conventional measures for binary outcome, even though such true model is presented in the clinical data. New statistical approaches or measures should be established to identify the casual true model from proxy models, especially for those in proxy models with extra noises and/or missing causal predictors. [ABSTRACT FROM AUTHOR]

Background: Clinical prediction models are useful in estimating a patient's risk of having a certain disease or experiencing an event in the future based on their current characteristics. Defining an appropriate risk threshold to recommend intervention is a key challenge in bringing a risk prediction model to clinical application; such risk thresholds are often defined in an ad hoc way. This is problematic because tacitly assumed costs of false positive and false negative classifications may not be clinically sensible. For example, when choosing the risk threshold that maximizes the proportion of patients correctly classified, false positives and false negatives are assumed equally costly. Furthermore, small to moderate sample sizes may lead to unstable optimal thresholds, which requires a particularly cautious interpretation of results.Main Text: We discuss how three common myths about risk thresholds often lead to inappropriate risk stratification of patients. First, we point out the contexts of counseling and shared decision-making in which a continuous risk estimate is more useful than risk stratification. Second, we argue that threshold selection should reflect the consequences of the decisions made following risk stratification. Third, we emphasize that there is usually no universally optimal threshold but rather that a plausible risk threshold depends on the clinical context. Consequently, we recommend to present results for multiple risk thresholds when developing or validating a prediction model.Conclusion: Bearing in mind these three considerations can avoid inappropriate allocation (and non-allocation) of interventions. Using discriminating and well-calibrated models will generate better clinical outcomes if context-dependent thresholds are used. [ABSTRACT FROM AUTHOR]