Advanced Epidemiological Methods Seminar, 18-21 May 2015

Posted on Wed, Nov 26 2014 14:33:00




Dr. Matthew Fox of the Department of Epidemiology and the Center for Global Health and Development at Boston University will be presenting an intensive 4-day course on advanced epidemiological methods at Stellenbosch University under the auspices of the South African DST/NRF Centre for Epidemiological Modeling and Analysis (SACEMA), 18-21 May 2015. The course will take place from 9 am to 15:30 pm daily at STIAS (Stellenbosch Institute for Advanced Study), adjacent to SACEMA.

The course fee, including refreshments, lunches and social events, is R4000 for early bird registration by Saturday 28 February 2015, and R5000 for later registration, closing on Friday 1 May 2015. Of this, R500 is a non-returnable registration fee. For international participants, the course fee is 450 euros for early bird registration, and 500 euros for late registration. Of this, 50 euros is a non-returnable registration fee. Accommodation, breakfast and dinner is not included in the course fee, but accommodation packages may be negotiated through SACEMA.


Contact the SACEMA Research Manager, Ms Lynnemore Scheepers, at (and copy to Matthew Fox <>), or phone: +27(0)21 808 2589.


Matthew Fox, DSc, MPH, is Associate Professor in the Center for Global Health & Development and in the Department of Epidemiology at Boston University. Dr. Fox joined the Center in 2001. Before joining Boston University, he was a Peace Corps volunteer in the former Soviet Republic of Turkmenistan. His research interests include treatment outcomes in HIV-treatment programs, infectious disease epidemiology (with specific interests in HIV, pneumonia, and malaria), and epidemiological methods. Dr. Fox is currently working on ways to improve retention in HIV-care programs in South Africa from the time of testing HIV-positive through long-term treatment. Dr. Fox also does research on quantitative sensitivity analysis and recently co-authored a book on these methods, Applying Quantitative Bias Analysis to Epidemiologic Data. He currently teaches a third-level epidemiological methods class. Dr. Fox is a graduate of the Boston University School of Public Health with a master's degree in epidemiology and biostatistics and a doctorate in epidemiology.

Course Overview
Introductory and intermediate courses in epidemiological methods teach students the concepts needed to begin a career conducting valid epidemiological research; however these courses typically only briefly cover the causal models that should underlie the design of valid epidemiological studies. We will use these models as a jumping-off point to begin rethinking what we have already learned and to go further in our understanding of basic concepts of measures of effect, confounding, misclassification and selection bias. From there we will begin to question the implications of various sources of bias in our studies and we will work through novel methods and approaches for doing more than simply speculating about these biases. We will then finish by exploring the basic statistics used in epidemiological research and we will correct misunderstandings about what these statistics can tell us.

Throughout the course we will focus on the core concepts of validity and precision and will further develop our understanding of these central concepts. We will emphasize the development of skills that every doctoral level epidemiologist should have, skills that are both practical and marketable. Note that this course is not offered for any credit. It is a course designed to help doctoral level and advanced master's students advance their skills.

A rough sketch of the session titles for each day have been provided below in the timetable but are subject to change.

Students are expected to prepare fully for class by reading the material ahead of time. There will be several readings per day, listed below. The readings for this course are challenging. You will not understand everything you read and you will need to read the articles more than once before coming to class. You will struggle if you wait until the last minute or do not read the material. The class can only function if you are prepared and have challenged yourself to think about the material. Come to class with questions about what you do not understand

Meeting time and place
Registration will take place at 8:30 am on Monday 18 May, and course sessions will begin every day at 9 am, with two sessions per day, with brief coffee/tea breaks.





Ioannidis JP. Why most published research findings are false. PLoS Med 2005;2(8):e124.


Greenland S and Robins JM. Identifiability, exchangeability, and epidemiological confounding. Int J Epidemiol 1986;15:412-418


Greenland S, Pearl J, and Robins JM. Causal diagrams for epidemiologic research. Epidemiology 1999;10:37-48.


Hernan MA, Brumback B, Robins JM. Marginal structural models to estimate the causal effect of zidovudine on the survival of HIV-positive men. Epidemiology 2000;11(5):561-570.


Rothman KJ. Modern Epidemiology, 1st Edition. Chapter 15 - Interaction between Causes. Little, Brown, and Company, Boston, MA: 1986. pp 311-326.


Jurek AM, Greenland S, Maldonado G et al. Proper interpretation of non-differential misclassification effects: expectations vs observations. Int J Epidemiol 2005;34(3):680-687.


Greenland S. Randomization, Statistics, and Causal Inference. Epidemiology 1990;1:421-429.


Goodman S N. Toward Evidence-Based Medical Statistics. 1: The P Value Fallacy. Annals of Internal Medicine 1999;130(12): 995-1004. Paper available at

Poole C. Low P-Values or Narrow Confidence Intervals: Which Are More Durable? Epidemiology 2001;12(3): 291-294.






May 18th


May 19th



May 20th



May 21st


8.30 - 9.00h






9.00h - 12.00h


INTRODUCTION TO MODERN EPIDEMIOLOGY: Review of basic epidemiology and introduction to advanced epidemiologic concepts


STRUCTURAL APPROACHES TO BIAS: Directed Acyclic Graphs and the potential harms of statistical adjustment?


THREE CONCEPTS OF INTERACTION: What do we really mean by interaction?


THE ABUSED P-VALUE: Random error I: what's in a p-value? P-values or confidence intervals?

12.00h - 13.00h



13.00h - 16.00h


THE POTENTIAL OUTCOMES MODEL: Confounded definitions of confounding



NOVEL APPROACHES TO DEALING WITH CONFOUNDING: Propensity Scores and Marginal Structural Models






STATISTICAL ALTERNATIVES: Introduction to Bayesian thinking