Hidden Publication Bias in Epidemiologic Secondary Data Analysis

We highlight a particular type of publication bias unique to secondary data analysis, and particularly common in epidemiologic research. We begin by setting a reminder of the scientific method of inquiry, and—by analogy with the movement for full transparency in clinical trials—present arguments for reporting all results of secondary data analysis. We then describe the ways in which data dredging—a subtle form a p-hacking—can lead to a distorted scientific literature; we highlight prior research that has empirically demonstrated this. We conclude by arguing that in order to combat this bias, epidemiologists should move toward preregistering analyses, and epidemiologic journals should encourage this through the implementation of Registered Reports. Finally, we respond to some common criticisms of preregistration.

Life course epidemiology and analysis

Life course epidemiology approaches disease aetiology and prevention from the perspective of risk and protective factors that influence health and disease throughout the lifespan. The integration of a life course approach to epidemiologic research is central for identifying effective policies and programmes to promote population health and health equity. This chapter will introduce life course concepts and models and analytical approaches for research on life course determinants of health. It will discuss threats to causal inference, approaches for overcoming these difficulties, and future directions in life course epidemiology. For example, in addition to expanding epidemiologic research with a life course perspective to include people with diverse life experiences, new areas of development include life course research extending beyond one human lifespan to include intergenerational and transgenerational life course research, as well as the application of innovative methods.

Results of analytic and epidemiologic research of external risk factors for multiple sclerosis among moscow population

Results of epidemiologic reseach of environmental risс factors for multiple sclerosis among Moscow population are given. It was shown, that provoking factors for development or exacerbation this pathology are infections and stress situations. Diet habits, chronic bacterial infections of respiratory tract are significant too. The obtained results can be used in development of a certain diet and preventive measures.

591The “Big 6”: A purpose-based framework for motivating and teaching epidemiologic methods

Focus of Presentation Many beginning students in health-related fields do not understand the types of questions that epidemiology can address. This results in underappreciation of the relevance of epidemiology. Even students dedicated to learning epidemiology and/or medicine have difficulty identifying the common features of disparate research questions and hence are limited in their ability to critique research. The reasons for these limitations are multifold, but we believe that traditional approaches of teaching epidemiology □ by study design □ is a substantial contributor. To better promote and deepen understanding of epidemiology, we have developed a purpose-based teaching approach called the “Big 6”. Findings In courses aimed towards graduate students in epidemiology and medical students, we now introduce what epidemiologic research can do and how to perform it according to the general purpose/goal/objective of research. We focus on six of the most common purposes/goals/objectives - the “Big 6”. Conclusions Introducing epidemiology according to general purposes of research (the “Big 6”) gives students a framework to understand the relevance of epidemiology and rapidly critique the validity of epidemiologic research. Key messages A purpose-based approach to teaching epidemiology may be more engaging and promote better understanding and application of epidemiologic methods.

Invited Commentary: Standards, Inputs, and Outputs—Strategies for Improving Data-Sharing and Consortia-Based Epidemiologic Research

Data-sharing improves epidemiologic research, but the sharing of data frustrates epidemiologic researchers. The inefficiencies of current methods and options for data-sharing are increasingly documented and easily understood by any study group that has shared its data and any researcher who has received shared data. In this issue of the Journal, Temprosa et al. (Am J Epidemiol. XXX(XX):XXX–XXX) describe how the Consortium of Metabolomics Studies (COMETS) developed and deployed a flexible analytical platform to eliminate key pain points in large-scale metabolomics research. COMETS Analytics includes an online tool, but its cloud computing and technology are the supporting rather than the leading actors in this script. The COMETS team identified the need to standardize diverse and inconsistent metabolomics and covariate data and models across its many participating cohort studies, and then developed a flexible tool that gave its member studies choices about how they wanted to meet the consortium’s analytical requirements. Different specialties will have different specific research needs and will probably continue to use and develop an array of diverse analytical and technical solutions for their projects. COMETS Analytics shows how important—and enabling—the upstream attention to data standards and data consistency is to producing high-quality metabolomics, consortia-based, and large-scale epidemiology research.