Dr. Greenland is Professor of Epidemiology, UCLA School of Public Health, and Professor of Statistics, UCLA College of Letters and Science.  Dr. Greenland is considered a leading authority on quantitative methods and statistical theory in epidemiology.

Major Discipline:
Epidemiology and Statistics

Current Research Interest:

Epidemiologic methodology; statistical methods for epidemiologic data; epidemiologic assessment of medicines and medical technology; foundations of nonexperimental inference.

Dr. Greenland's Resume (PDF)

Email Address:


List of corrections for Modern Epidemiology 3rd edition


Epidemiology 200C. Analysis (Methods III) (4)  Designed to provide the student with the basic concepts, principles, and methods of epidemiologic data analysis. Teaches statistical concepts essential to core training of epidemiology majors. This is the third of the sequence of the three-quarter Epi 200 course.


Epidemiology 203. Topics in Theoretical Epidemiology (2) Lecture, two hours. Selected topics from current research areas in epidemiologic theory and quantitative methods. Topics selected from biologic models, epidemiologic models, problems in inference, model specification problems, design issues, analysis issues, and confounding. May be repeated for credit with consent of instructor. S/U grading.

Epidemiology 204/Statistics 243. Logic, Causation, and Probability
(4) Lecture, four hours. Preparation: two terms of statistics or probability and statistics. Recommended requisite: course 201 B. Principles of deductive logic and causal logic using counterfactuals. Principles of probability logic and probabilistic induction. Causal probability logic using directed acyclic graphs. S/U or letter grading.  

Epidemiology 211/Biostat 211. Statistics for Epidemiology (4)  The objective is to enable students to conduct thoughtful analysis of epidemiologic data using basic tabular and graphical analysis modules in common statistical packages. The immediate aim is to prevent the usual rote significance-testing analyses that students produce after emerging from first-year courses. The lectures follow in sequence the topics Modern Epidemiology.

Epidemiology 212/Biostat 209. Statistical Modeling in  Epidemiology
(4)  The course covers both Regression Models and Regression Modeling.

Gauss programs and output for bias analysis via missing-data methods: