Tailored Bayes: a risk modeling framework under unequal misclassification costs

Solon Karapanagiotis, Umberto Benedetto, Sach Mukherjee, Paul D W Kirk, Paul J Newcombe

Research output: Contribution to journalArticle (Academic Journal)peer-review

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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 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 tumor classification task and demonstrate the improvement in predictive performance over standard methods.
Original languageEnglish
Article numberkxab023
Number of pages23
Early online date7 Aug 2021
Publication statusE-pub ahead of print - 7 Aug 2021


  • Bayesian inference
  • binary classification
  • misclassification costs
  • tailored Bayesian methods


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