Value-of-Information Analysis for External Validation of Risk Prediction Models.

ABSTRACT:

Background

A previously developed risk prediction model needs to be validated before being used in a new population. The finite size of the validation sample entails that there is uncertainty around model performance. We apply value-of-information (VoI) methodology to quantify the consequence of uncertainty in terms of net benefit (NB).

Methods

We define the expected value of perfect information (EVPI) for model validation as the expected loss in NB due to not confidently knowing which of the alternative decisions confers the highest NB. We propose bootstrap-based and asymptotic methods for EVPI computations and conduct simulation studies to compare their performance. In a case study, we use the non-US subsets of a clinical trial as the development sample for predicting mort

SUBMITTER: Sadatsafavi M 

PROVIDER: S-EPMC10336716 | biostudies-literature | 2023 Jul

REPOSITORIES: biostudies-literature