Tailored Bayes: a risk modelling framework under unequal misclassification costs

TL;DR

This paper introduces Tailored Bayes, a Bayesian framework designed to optimize predictive accuracy in healthcare risk models by accounting for unequal misclassification costs, outperforming standard methods in simulations and real-world applications.

Contribution

The paper presents a novel Bayesian inference framework that explicitly incorporates unequal misclassification costs into model fitting for binary classification in healthcare.

Findings

Tailored Bayes improves predictive performance over standard methods.

Simulation studies show when TB outperforms traditional Bayesian approaches.

Real-world applications demonstrate TB's effectiveness in healthcare risk prediction.

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 work, we present Tailored Bayes (TB), a novel Bayesian inference framework which "tailors" model fitting to optimise predictive performance with respect to unbalanced misclassification costs. We use simulation studies to showcase when TB is expected to outperform standard Bayesian methods in the context of logistic regression. We then apply TB to three real-world applications, a cardiac surgery, a breast cancer prognostication task and a breast cancer tumour classification task, and demonstrate the improvement in predictive performance over standard methods.