Qini Curves for Potential Impact Assessment of Risk Predictive Models Informing Intervention Policies

Predictive models in medicine help make decisions about which individual to treat with a given therapeutic or preventive intervention. Before being tested in large field studies and recommended for clinical adoption, it is important to evaluate not only their statistical accuracy but also the impact they may have when used to inform health intervention policies. We aim to provide simple methods for the potential impact assessment of health intervention policies based on predictive models.

We propose an analytic framework based on Qini curves wherein prediction-based policies are analyzed on 2 impact endpoints: (1) the fraction of the population that would be selected for the intervention (coverage) and (2) the effect on the clinical outcomes of interest (disutility). The drivers of values are the disease prevalence, the predictive performance of the model, and the effectiveness of the intervention.

We present simple formulas for calculating coverage and disutility from either observational or randomized controlled data. We illustrate possible value measures arising from geometrical properties on the Qini plane: delta coverage and disutility, number needed to treat, and integrated difference between Qini curves. We show the applicability of the Qini analysis by providing examples about the prevention of falls in older adults and prevention of secondary cardiovascular events with pioglitazone.

Coverage and disutility capture key value components of prediction-based policies. The method can be used for comparing models or tuning risk thresholds for managing trade-offs between conflicting objectives (eg, clinical benefits, side effects, and healthcare resources).