Analytics, trial methods and modeling

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A common criticism of precision medicine is that it may offer treatment benefits to individuals, but fail to address broader population disparities in health; precision medicine may even exacerbate health disparities if disadvantaged populations have disproportionately low utilization of novel screening and treatment technologies.1,2 In response to this criticism, the charge of the Analytics and Modeling (A&M) Core is to bridge the divide between individual-level precision medicine and population-level health disparities by leveraging two major strengths at Stanford University: (i) expertise in using omics data at the individual level to design and evaluate disease screening and treatment strategies, and (ii) expertise in computer modeling methods collectively referred to as “systems science”, which integrate data from individuals (omics data, clinical data, behavioral survey data) with data on key contextual factors (social and economic barriers to healthcare access, cultural factors, environmental factors, health-related policies) to study the distribution of health in the population. Leveraging Stanford's unique expertise in these areas, the A&M core will pursue the following objectives: (1) to integrate data from the Native American arthritis and multi-ethnic breast cancer R01 projects into problem–specific systems science models to perform leverage point analysis, which involves integrating individual-level omics data with clinical, biological, and contextual data to identify which precision medicine interventions are best for improving individual patient outcomes, population-level health disparities, or both;5,6 (2) conduct cost-effectiveness analyses for the Latino obesity R01 project, by studying the cost-effectiveness of integrating personalized –omics profiling (iPOP) into a multi-component, multi-setting intervention to reduce weight gain among Latino youth; and (3) serve as a center for excellence for the deployment of innovative data integration, analysis and modeling strategies to bridge the divide between precision medicine and population health. Our models will integrate multi-level data on omic risk variations within each studied population, social and cultural barriers that influence screening and treatment, screening performance, patient responses to test results, clinical gains to therapeutic efficacy from screening results, patient responses to suggested therapies, and patient outcomes from therapy. By integrating these diverse data into simulation models that link these individual-level factors to population disparities in disease risk and outcomes, we can identify the impact of altering key components – the levers – of patient outcomes and of population disparities, to inform the targeting and development of future precision medicine programs. Use systems science techniques, the A&M Core will create, validate, and implement models that identify how screening and treatment based on omics data can most effectively and cost-effectively reduce differential risk among vulnerable populations, in the interest of eventually leading precision medicine intervention strategies that will reduce population disparities in disease risk and treatment outcomes.