4.5 Article

Tailored Bayes: a risk modeling framework under unequal misclassification costs

Journal

BIOSTATISTICS
Volume 24, Issue 1, Pages 85-107

Publisher

OXFORD UNIV PRESS
DOI: 10.1093/biostatistics/kxab023

Keywords

Bayesian inference; Binary classification; Misclassification costs; Tailored Bayesian methods

Funding

  1. Medical Research Council [MC_UU_00002/9, MC_UU_00002/13, MR/R014019/1]
  2. National Institute for Health Research Bristol Biomedical Research Centre (NIHR Bristol BRC)
  3. National Institute for Health Research (Cambridge Biomedical Research Centre at the Cambridge University Hospitals NHS Foundation Trust)
  4. Alan Turing Institute under the EPSRC [EP/N510129/1]
  5. RESCUER project
  6. European Union [847912]
  7. Medical Research Council [MR/R014019/1] Funding Source: researchfish

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Risk prediction models are crucial in healthcare, but the assumption that the costs of different classification errors are equal may not be valid in many healthcare applications. Tailored Bayes (TB) is introduced as a Bayesian framework to optimize predictive performance with respect to unbalanced misclassification costs, showing improvement over standard methods in simulation studies and real-world applications.
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.

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