Preferences for Predictive Model Characteristics among People Living with Chronic Lung Disease: A Discrete Choice Experiment.

ABSTRACT: Background. Patients may find clinical prediction models more useful if those models accounted for preferences for false-positive and false-negative predictive errors and for other model characteristics. Methods. We conducted a discrete choice experiment to compare preferences for characteristics of a hypothetical mortality prediction model among community-dwelling patients with chronic lung disease recruited from 3 clinics in Philadelphia. This design was chosen to allow us to quantify "exchange rates" between different characteristics of a prediction model. We provided previously validated educational modules to explain model attributes of sensitivity, specificity, confidence intervals (CI), and time horizons. Patients reported their interest in using prediction models them

SUBMITTER: Weissman GE 

PROVIDER: S-EPMC7395892 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature