Uncertainty and the Value of Information in Risk Prediction Modeling.

ABSTRACT:

Background

Because of the finite size of the development sample, predicted probabilities from a risk prediction model are inevitably uncertain. We apply value-of-information methodology to evaluate the decision-theoretic implications of prediction uncertainty.

Methods

Adopting a Bayesian perspective, we extend the definition of the expected value of perfect information (EVPI) from decision analysis to net benefit calculations in risk prediction. In the context of model development, EVPI is the expected gain in net benefit by using the correct predictions as opposed to predictions from a proposed model. We suggest bootstrap methods for sampling from the posterior distribution of predictions for EVPI calculation using Monte Carlo simulations. We used subsets of data of various

SUBMITTER: Sadatsafavi M 

PROVIDER: S-EPMC9194963 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature