Keegan Korthauer

Mendelian randomization (MR) is a causal inference method that allows biostatisticians to leverage DNA measurements to study causal effects with only observed data. Recent advancements including two-sample summary-level mendelian randomization (TSSLMR) and the data source IEU OpenGWAS database have lowered the barrier for conducting MR studies and opened the opportunity to mine causal effects. In the first part of the thesis, I show that there is a mismatch between the characteristics of modern TSSLMR data and how articles that propose popular TSSLMR models conduct their simulations. Next, I propose my solution: a data driven simulation framework for MR data that aims to be realistic, interpretable and easy to use thanks to a complementary R package implementation. As for the results, I show that models perform far better in literature-based simulations compared to more realistic simulations based on my proposed framework. Lastly, I warn that the mismatch between simulated and real data along with the obtained results may lead researchers to have over optimistic expectations about models performance in real applications.

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Omics-based tests (OBTs) combine high-dimensional omics features into clinical prediction modelsthat 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 clinicalevaluation. However, their performance assessment is often limited to classification metrics such assensitivity and specificity, with little regard for formal analysis of clinical decision-making. Decisioncurve 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. InDCA, the best clinical decision strategy, such as diagnosing or treating based on an OBT, is the onethat maximizes the concept of net benefit: the net number of true positives (or negatives) providedby 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 particularlyprone to chance results. In this context, the present work develops parametric Bayesian approachesto DCA that allow uncertainty quantification around four fundamental concerns when evaluatingOBT-guided clinical decision strategies: (i) which strategies are clinically useful, (ii) what is thebest available decision strategy, (iii) direct pairwise comparisons between strategies, and (iv) whatis the consequence of the current level of uncertainty. We evaluate the methods using simulationstudies 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 freelyavailable in the bayesDCA R package. Ultimately, the Bayesian DCA workflow may help cliniciansand health policymakers make better-informed decisions when choosing and implementing clinicaldecision strategies based on OBTs.

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