Since confounding obscures the real effect of the exposure, it is important to adequately address confounding for making valid causal inferences from observational data. Directed acyclic graphs (DAGs) are visual representations of causal assumptions that are increasingly used in modern epidemiology. They can help to identify the presence of confounding for the causal question at hand. This stru...

Most people want to know how they can improve their health by implementation of a proper diet. Therefore nutritional epidemiology studies, which correlate the intake of specific nutrients, food items, or dietary patterns with health outcomes, receive substantial interest in the media. However, the results of many nutritional epidemiology studies have not been replicated in subsequent studies. T...

In this article, we summarize the main takeaways from a symposium and hybrid virtual in-person participatory discussion focused on challenges of scale in understanding ecology management phyllosphere microbial communities. We provide an overview confounding effects spatial inference ecology, organization interactions phyllosphere, advances remaining gaps measuring colonization across scales, ep...

Prospective studies of the epidemiology of coronary heart disease (CHD) and cancer have shown that mortality from these diseases differs greatly among populations and that at least some of the differences are associated with differences in dietary habits [1, 2]. Mediterranean populations, for instance, are protected from CHD and certain cancers, and the particular composition of the Mediterrane...

What is Mendelian Randomization? The field of epidemiology has struggled to make headway in determining whether exposures are causal factors for complex diseases, largely because of the problems of confounding, reverse causation, and bias. To overcome the problems inherent in observational studies, epidemiologists have proposed using genetic variants as proxies for exposures.1,2 The idea is to ...

What is Mendelian Randomization? The field of epidemiology has struggled to make headway in determining whether exposures are causal factors for complex diseases, largely because of the problems of confounding, reverse causation, and bias. To overcome the problems inherent in observational studies, epidemiologists have proposed using genetic variants as proxies for exposures.1,2 The idea is to ...