Bias is defined as a systematic error which results in an incorrect or invalid estimate of the measure of association, and addressing bias in observational studies is a central goal of the field of epidemiology. The following exercise will introduce several different types of bias, discuss various ways of minimizing or eliminating it, as well as possible impact on study results.

Faculty highlight: Steven D. Stellman, PhD

Dr. Steven Stellman's research has included studies of tobacco-related cancers, dietary risk factors for cancer, and environmental factors in breast cancer. More recently, he has applied geographic information systems (GIS) in the studies of physical and mental health of Vietnam veterans in relation to exposure to Agent Orange and military combat, and served as the Research Director for The World Trade Center Health Registry through the NYC Department of Health.

For this module we will focus on a recent paper published by a team of investigators involved with the Long Island Breast Cancer Study Project, examining the association between home pesticide use and breast cancer. In particular, we will focus on the potential association between lawn/garden pesticide use and risk of breast cancer in an article by the study team led by Dr. Stellman and Dr. Susan Teitelbaum, a former student in our Department.

Read more about Dr. Stellman's work in the following articles

Good luck and have fun!