At increasingly high rate, genome-wide association and whole genome sequencing studies unravel genetic variants implicated in common diseases such as coronary heart disease, cancer, dementia and type 2 diabetes. One of the major promises is that these advances will lead to more personalized medicine, in which preventive and therapeutic interventions are targeted to individuals based on their genetic profiles.
There is increasing interest in the early adoption of novel applications and many commercial applications are already marketed without supporting empirical evidence. Already now, regulatory agencies like the US Food and Drug Administration face substantial gaps in empirical evidence, which hamper proper recommendations. The increasing interest in genomic medicine, the evidence gaps and the scarcity of research budgets are strong incentives to search for novel strategies that make the process of translation research more efficient and effective.
This project aims to investigate modelling approaches that can be used to predict the expected outcomes of empirical studies on the basis of published epidemiological and intervention studies. This approach can be used to 1) identify genomic applications that are promising and warrant further empirical research, and 2) fill in evidence gaps by identifying applications that are not expected to improve health or health care. When they are valid, precise and simple, modelling studies can optimize the process of translational research so that time and money are allocated to the most promising applications.
In this project, I will 1) characterize empirical studies in translational research in terms of the main outcome measures used and their key determinants; 2) develop simulation models that predict outcome measures; 3) investigate how accuracy and precision of the estimates vary with varying model complexity; and 4). investigate the generalizability of the modelling approaches.
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