Tailored Bayes: a risk modeling framework under unequal misclassification costs.

ABSTRACT: Risk prediction models are a crucial tool in healthcare. Risk prediction models with a binary outcome (i.e., binary classification models) are often constructed using methodology which assumes the costs of different classification errors are equal. In many healthcare applications, this assumption is not valid, and the differences between misclassification costs can be quite large. For instance, in a diagnostic setting, the cost of misdiagnosing a person with a life-threatening disease as healthy may be larger than the cost of misdiagnosing a healthy person as a patient. In this article, we present Tailored Bayes (TB), a novel Bayesian inference framework which "tailors" model fitting to optimize predictive performance with respect to unbalanced misclassification costs. We use simulation st

SUBMITTER: Karapanagiotis S 

PROVIDER: S-EPMC9748575 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature