Epidemiologic Methods

Degree students in this area of research are given the opportunity not only to study and apply advanced methodology but also to develop new methodology that may go on to shape the discipline’s future.

Epidemiologic methods refer to the systematic techniques and procedures used by epidemiologists to study the distribution and determinants of health-related states or events in specific populations. These methods help in understanding the causes of diseases, identifying risk factors, and developing strategies for prevention and control. 

This area provides training in the development and application of new methods in epidemiologic research. Students learn to use and justify classical epidemiologic methods in study design, data analysis, and interpretation of results. Students also receive training in biostatistical areas most relevant to epidemiologic research. Recent innovations in epidemiologic methodology are introduced through advanced courses and tutorials. The Department of Epidemiology at the Harvard T. H. Chan School of Public Health has a long history of developing novel methods in epidemiology. Over the past decades, this has included developing state-of-art methodology concerning novel study designs, approaches to causal inference, longitudinal data, the assessment of mechanisms, the evaluation of measurement error, genetic analysis, and the very foundations of epidemiologic concepts.

Highlights from this program include:

• Learn to use and justify classical epidemiologic methods in study design, data analysis, and interpretation of results
• Receive training in biostatistical areas most relevant to epidemiologic research. Recent innovations in epidemiologic methodology are introduced through advanced courses and tutorials
• Doctoral students conduct research with faculty members in the development of new methodologies and in novel applications of existing methodologies. Those enrolling in this area of interest ordinarily have completed four semesters of college calculus and one semester of linear algebra
• Collaborate with researchers working on causal inference in epidemiology and allied sciences
• Collaborate with a cross-departmental group of epidemiologists and biostatisticians working on methods to adjust for bias due to exposure measurement error in nutritional, environmental and occupational health research.

Students with degrees in computer science, mathematics or statistics are especially encouraged to apply to this area.

Learn more about our faculty below: