  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.
