Advanced topics in Epidemiologic Research

Catalog description

The overarching goal of this course is to expose doctoral students in epidemiology to advanced epidemiologic and statistical research methods and theories that are limitedly or not otherwise covered in courses available in the curriculum. Topics that will be covered include reporting guidelines and best practices for reporting statistical methods and results, handling missing data, purposeful selection and application of propensity scores, selected topics in longitudinal and clustered data analysis, contemporary topics in statistical inference and use of p-values and other Frequentist statistical methods, Bayesian theory and inference, and topics selected in collaboration with students and the Graduate Group in Epidemiology and Biostatistics (GGEB) each term. This course is intended for doctoral students in the PhD program in Epidemiology. However, students from other graduate groups are welcome, as long as they meet the pre-requisites; such students are welcome during any year of study. Three learning objectives have been developed for this course; (i) provide students with an understanding of modern and cutting-edge quantitative methods, advanced topics, and best practices in epidemiologic, statistical, and biomedical research; (ii) develop students competence and confidence in statistical programing to support accurate and reproducible epidemiologic and biostatistical analyses; (iii) improve the ability of students to make informed decisions regarding the selection of analytic methods in their individual and collaborative research projects. This course emphasizes the following core competencies: knowledge within program area (epidemiologic and biostatistical methods); research skills (study planning, critically appraising published research); quantitative and computational methodologies (data manipulation, data analysis, statistical coding and debugging, Bayesian…