Epidemiology
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Recommendations for reporting instrumental variable analyses often include presenting the balance of covariates across levels of the proposed instrument and levels of the treatment. However, such presentation can be misleading as relatively small imbalances among covariates across levels of the instrument can result in greater bias because of bias amplification. ⋯ These plots can also provide relevant comparisons of different proposed instruments considered in the same data. Adaptable code is provided for creating the plots.
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Ongoing breast cancer screening programs can only be evaluated using observational study designs. Most studies have observed a reduction in breast cancer mortality, but design differences appear to have resulted in different estimates. Direct comparison of case-control and trial analyses gives more insight into this variation. Here, we performed case-control analyses within the randomized UK Age Trial. ⋯ Observational studies, and particularly case-control studies, are an important monitoring tool for breast cancer screening programs. The focus should be on diminishing bias in observational studies and gaining a better understanding of the influence of study design on estimates of mortality reduction.
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Big Data has increasingly been promoted as a revolutionary development in the future of science, including epidemiology. However, the definition and implications of Big Data for epidemiology remain unclear. We here provide a working definition of Big Data predicated on the so-called "three V's": variety, volume, and velocity. ⋯ We suggest that as more sources of diverse data become publicly available, the ability to combine and refine these data to yield valid answers to epidemiologic questions will be invaluable. We conclude that while epidemiology as practiced today will continue to be practiced in the Big Data future, a component of our field's future value lies in integrating subject matter knowledge with increased technical savvy. Our training programs and our visions for future public health interventions should reflect this future.
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The prevalence of overweight and obesity is rising globally and together they constitute a major risk factor for coronary heart disease (CHD). Previous estimates of direct effects of high body mass index (BMI) on CHD did not consider an interaction between BMI and its mediators and did not include inflammatory biomarkers as potential mediators. ⋯ Metabolic mediators explain about half of the adverse effects of high BMI on CHD. The role of inflammatory and prothrombotic biomarkers is much smaller than that of metabolic factors.