Approaches to developing clinically useful Bayesian risk prediction models
type: CreativeWork
description: Prediction of the presence of disease (diagnosis) or an event in the future course of disease (prognosis) becomes increasingly important in the current era of personalised medicine. Both tasks (diagnosis and prognosis) are supported using (risk) prediction models. Such models usually combine multiple variables by using different statistical and/or machine learning approaches. Recent advances in prediction models have improved diagnostic and prognostic accuracy, in some cases surpassing the performance of clinicians. However, evidence is lacking that deployment of these models has improved care and patient outcomes. That is, their clinical usefulness is debatable. One barrier to demonstrating such improvement is the basis used to evaluate their performance. In this thesis, we explore methods for developing (building and evaluating) risk prediction models, in an attempt to create clinically useful models. We start by introducing a few commonly used metrics to evaluate the predictive performance of prediction models. We then show that a model with good predictive performance is not enough to guarantee clinical usefulness. A well performing model can be clinically useless, and a poor model valuable. Following recent line of work, we adopt a decision theoretic approach for model evaluation that allows us to determine whether the model would change medical decisions and, if so, whether the outcome of interest would improve as a result. We then apply this approach to investigate the clinical usefulness of including information about circulating tumour DNA (ctDNA) when predicting response to treatment in metastatic breast cancer. ctDNA has been proposed as a promising approach to assess response to treatment. We show that incorporating trajectories of circulating tumour DNA results in a clinically useful model and can improve clinical decisions. However, an inherit limitation to the decision theoretic approach (and related ones) is that model building and evaluation are don…
abstract: Prediction of the presence of disease (diagnosis) or an event in the future course of disease (prognosis) becomes increasingly important in the current era of personalised medicine. Both tasks (diagnosis and prognosis) are supported using (risk) prediction models. Such models usually combine multiple variables by using different statistical and/or machine learning approaches. Recent advances in prediction models have improved diagnostic and prognostic accuracy, in some cases surpassing the performance of clinicians. However, evidence is lacking that deployment of these models has improved care and patient outcomes. That is, their clinical usefulness is debatable. One barrier to demonstrating such improvement is the basis used to evaluate their performance. In this thesis, we explore methods for developing (building and evaluating) risk prediction models, in an attempt to create clinically useful models. We start by introducing a few commonly used metrics to evaluate the predictive performance of prediction models. We then show that a model with good predictive performance is not enough to guarantee clinical usefulness. A well performing model can be clinically useless, and a poor model valuable. Following recent line of work, we adopt a decision theoretic approach for model evaluation that allows us to determine whether the model would change medical decisions and, if so, whether the outcome of interest would improve as a result. We then apply this approach to investigate the clinical usefulness of including information about circulating tumour DNA (ctDNA) when predicting response to treatment in metastatic breast cancer. ctDNA has been proposed as a promising approach to assess response to treatment. We show that incorporating trajectories of circulating tumour DNA results in a clinically useful model and can improve clinical decisions. However, an inherit limitation to the decision theoretic approach (and related ones) is that model building and evaluation are don…
author: Karapanagiotis, Solon
language: eng
keywords: Tailored Bayesian methods, Bayesian inference, misclassification costs,, binary classification, breast cancer, risk prediction models, circulating tumour DNA
license: https://www.repository.cam.ac.uk/bitstreams/35dcbcb1-572c-454b-af28-83893344c3b9/download
license: https://creativecommons.org/licenses/by-nc/4.0/
date published: 2021-04-14
date modified: 2026-07-22T22:23:56Z
provider: Cambridge University
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: University of Cambridge

PropertyValue
property ID: DOI
value: 10.17863/CAM.76594