UBC Theses and Dissertations
Evaluating omics-based tests with Bayesian Decision Curve Analysis Netto Flores Cruz, Giuliano
Omics-based tests (OBTs) combine high-dimensional omics features into clinical prediction models that predict diagnosis, prognosis, or treatment effects. Past incidences of premature implementa- tion of OBTs into clinical trials have demonstrated the need for increased rigour in their clinical evaluation. However, their performance assessment is often limited to classification metrics such as sensitivity and specificity, with little regard for formal analysis of clinical decision-making. Decision curve analysis (DCA) complements classification metrics by combining classical assessment of pre- dictive performance with the consequences of using a test or model to guide clinical decisions. In DCA, the best clinical decision strategy, such as diagnosing or treating based on an OBT, is the one that maximizes the concept of net benefit: the net number of true positives (or negatives) provided by a given clinical decision strategy. Before reaching real patients, we must be sufficiently confi- dent that new OBTs actually provide superior clinical decision strategies, as compared to default, standard-of-care strategies. Trained on hundreds to thousands of features, OBTs are particularly prone to chance results. In this context, the present work develops parametric Bayesian approaches to DCA that allow uncertainty quantification around four fundamental concerns when evaluating OBT-guided clinical decision strategies: (i) which strategies are clinically useful, (ii) what is the best available decision strategy, (iii) direct pairwise comparisons between strategies, and (iv) what is the consequence of the current level of uncertainty. We evaluate the methods using simulation studies and present a comprehensive case study. We also provide an application to a recently- developed OBT for multi-cancer early detection. Software implementation of the method is freely available in the bayesDCA R package. Ultimately, the Bayesian DCA workflow may help clinicians and health policymakers make better-informed decisions when choosing and implementing clinical decision strategies based on OBTs.
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