New health studies appear in the news every day. But how do we know if the latest drug or intervention actually has merit? Offering crucial tools for evaluating the significance or impact of public health research and interventions, biostatistics is the key.
With demand for biostatisticians far exceeding supply, current and future public health research is at risk. The Certificate in Applied Biostatistics seeks to address this shortage by offering specialized interdisciplinary training in biostatistics to MPH students from other disciplines.
Graduates learn to interpret research results and convey them in clear written and oral presentations suitable for non-expert audiences—a necessary skill for translating science into action. This program augments a graduate’s discipline and opens new professional opportunities, such as serving as a statistical consultant or as a technical resource person in field and programmatic studies.
Applied Biostatistics is open to Columbia MPH students in:
Applicants should have one semester of calculus and a score in the 75th percentile or higher on the Quantitative Reasoning Section of the GRE. Due to course requirements, the certificate is most compatible for students in Environmental Health Sciences, Epidemiology, and Population & Family Health.
Visit the Certificates Database to learn more about core and credit requirements.
Categorical Data Analysis
This course is a comprehensive overview of methods of analysis for binary and other discrete response data, with applications to epidemiological and clinical studies. Topics discussed include 2×2 tables, m×2 tables, tests of independence, measures of association, power and sample size determination, stratification and matching in design and analysis, interrater agreement, and logistic regression analysis.
Applied Regression II
This course introduces the statistical methods for analyzing censored data, non-normally distributed response data, and repeated measurements data that are commonly encountered in medical and public health research. Topics include estimation and comparison of survival curves, regression models for survival data, logit models, log-linear models, and generalized estimating equations. Examples are drawn from the health sciences.