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Analysis of Observational Studies for Emerging Health Care Applications

Epidemiology, the study of patterns, causes, and effects of health and disease conditions in defined populations, is a cornerstone of public health. By identifying risk factors for disease and targets for preventive healthcare, it shapes policy decisions and evidence-based practice. Due to the constraints of ethnics and costs, most epidemiologic research focuses on observational (or non-experimental) studies. Our approach is to combine the advances in the design of observational studies in epidemiology with corresponding advances in statistical methodologies for the analysis of observational data, controlling for confounding factors and other sources of systematic errors. It is expected to yield novel efficient study designs and analysis methods that can capitalize on the recent explosion of digital data such as electronic health records, medical billings and on-line recruitments, while providing valid inference that adjusts for possible confounding and selection bias.

We review in this connection an ongoing observational cohort study that uses structural equation modeling in optimizing the latent exposure and outcome variables and account for the issue of negative confounding. This is joint work with Philippe Grandjean at the Harvard School of Public Health.


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