Methods Core C

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SUMMARY – CORE C The Statistics and Networks Methods Core (Core C) of this PPG will actively collaborate with each of the projects by helping them use statistical, network, and computational methods in their research; by advancing statistical and network methods to expand the scope of their accomplishments; and by helping them solve any unexpected problems that arise. Core C staff will collaborate with Project Leads on study designs, and will supervise Core B programmers to overcome challenges and implement methods that Core C develops inside the secure computing environment at The Dartmouth Institute, so that these can be used to analyze the patient identifiable data relevant to each project. Motivated by the recent surge of interest in health and health care for Alzheimer’s disease and related dementias (ADRD), a growing concern for health disparities and their exacerbation by algorithmic bias, and advances in statistical methods and network analysis, Core C will advance methods that will make all four projects more effective and impactful. Core C will focus on: (1) statistical models for complex data structures that expand understanding of disparities and intersectional disparities, (2) development of networks that better capture the structure of relationships linking physicians of multiple specialties through the interdependencies in their medical practice, and (3) construction of novel quality of care measures and models of network data. These methods will be applied to patients with claims- based diagnoses of ADRD or who are at risk for developing ADRD. A common new theme in this renewal PPG is the use of comparative-effectiveness research, as it overcomes bias and enhances statistical power to quantify disparities and variations by modeling hierarchically structured and other forms of dependent data and by forming inferences that are causally-defendable. Core C key personnel have made substantial progress in establishing methods that use directional relationship information from patient-physician encounters in Medicare claims to form innovative physician networks with directed edges. They will enhance this work by using specialty and services-performed data to establish even more informative networks and network-based measures of access to care and other important aspects of health care. Statistical models will also be devised that use these networks to explain additional variation in outcomes and disparities, while longitudinal models of the network itself will provide insights into trends in and the dynamic association of factors affecting ADRD diagnosis and care. These innovations will allow the PPG-supported projects to delve deeper than would be possible otherwise. Results and products (e.g., software) will be widely disseminated to research and stakeholder communities. Core C will have a major supportive and innovative impact on the research of the four projects and on the wider research communities working in ADRD, health disparities, and variations in care with a goal of improving the U.S. health system.