Epidemiology for Bioinformaticians
Chloe Anya Mirzayi,Levi Waldron CUNY Graduate School of Public Health and Health Policy
Abstract
Concepts of causal inference in epidemiology have important ramifications for studies across bioinformatics and other fields of health research. In this workship, we introduce basic concepts of epidemiology, study design, and causal inference for bioinformaticians. Emphasis is placed on addressing bias and confounding as common threats to assessing a causal pathway in a variety of study design types and when using common forms of analyses such as GWAS and survival analysis. Workshop participants will have the opportunity to create their own structural causal models (DAGs) and use this model to determine how to assess an estimated causal effect. Examples using DESeq2, edgeR, and limma will be used to show how multivariable models can be fitted depending on the hypothesized causal relationship.
Presented successfully at BioC2020 to more than 100 people, updates that material by adding additional practical examples, clarifications based on participant feedback, and substantive revisions of existing examples.
Keywords: causal inference,epidemiology,confounding,selection bias