Enables students to apply the principles and methods learned in an academic setting through the preparation of a monograph that embodies original research.

Enables students to apply the principles and methods learned in an academic setting through the preparation of a monograph that embodies original research applicable to public health, incorporating a hypothesis that has been successfully evaluated with appropriate statistical and epidemiological techniques and is potentially publishable and has public health impact.

The course will include two overarching segments: elements of probability, and random variables and distributions.

PhD students only. Designed specifically for Epidemiology PhD students to learn statistical theory in the context of epidemiologic concepts and examples. The aim of the course is for students to understand the theories that underlie the statistical techniques used in epidemiologic research and to enhance critical think and integration of this material with broader epiemiologic principles.

Covers a wide variety of topics in epidemiologic methodology. Topics will include basic epidemiologic measures, confounding, misclassification, selection bias, types of case-control studies, Berkson of epidemiologic parameters. Prerequisite: EPI 530, EPI 534, BIOS 500, BIOS 501 (EPI 534 and BIOS 501 may be taken concurrently). Doctoral student section.

This course reviews the epidemiology of human reproductive function and the methodologic issues involved in studying reproduction. Topics include male and female infertility, pregnancy loss, the impact of infectious diseases on reproduction, contraceptive efficacy, unintended pregnancy, and environmental and occupational impacts on reproduction. Prerequisite: EPI 504 or EPI 530.

Prerequisites: Required: EPI 530 and BIOS 500; BIOS 501 or BIOS 591P; also preferred: EPI 539, EPI 540 or EPI 545; or instructor?s permission. Students will gain experience reading, evaluating, and interpreting EPI studies on the health impact of workplace & environmental exposures. Students will understand & interpret the EPI literature. Skills are developed through class lectures, assigned readings, & case studies. Most case studies require data analysis though the focus of the class is on conceptual issues common in environmental EPI rather than on applied statistics. Cross listed with EPI 747.

This course provides a survey of modern topics in causal inference. Fundamental concepts in causal inference will be covered including: counterfactual random variables, assessing identifiability of causal effects, graphical frameworks, Gcomputation, inverse probability of treatment weighting, methods for efficient, doubly (multiply) robust estimation of causal effects, and causal mediation. Where possible, the course emphasizes the use of modern regression (e.g., machine learning) in causal effect estimation and provides an applied introduction to this area is provided as well.

This is a special topics course.

Presents discussions by invited guests, faculty, and students of special topics and research findings. PhD students only.