Biostatistical competency relates to the knowledge of Biostatistics methods and their application, such as descriptive statistics, inference and statistical modeling. Along with awareness of biostatistical principles, the program will inculcate in the students a critical thinking in the selection of the appropriate statistical technique (e.g., linear versus logistic regression, parametric versus semi-parametric modeling for survival data, or mixed effects versus generalized estimating equation models for longitudinal data).
The program will also build skills in the design of clinical (interventional) versus observational studies, data collection schemes and the analysis of the collected data plus interpretation and communication of the study results to public health practitioners both expert and non-expert in biostatistical methodology. A significant emphasis will be given to international issues affecting public health theory and practice as well of bioethics issues in research especially with respect to those arising in international or non-equitable settings.
Public Health Competence
Public Health competency refers to having a thorough understanding of the principles of screening and disease surveillance, prevention, observational and intervention studies, the local, national and global context of health problems, and the influence of cultural and social dimension of public health research and practice.
Computing and Data Management
The program will emphasize the appropriate methods for the design of data collection systems in the context of biomedical research (both pre-clinical and clinical, including clinical trials and observational studies), as well as the proper management, analysis and interpretation of these data.
In addition to the collection, management and analysis of biomedical data, the program will provide a solid computational background to graduating students. Instruction will be primarily in SAS (The SAS Institute, Cary, NC) and R (www.r-project.org). However, other packages (e.g., STATA) and data management packages (e.g., REDCap) will be covered. Emphasis will be given to data analysis as well as quality control and data generation (simulations).
The overarching philosophy of the MS Biostatistics program is learning by doing. This approach will culminate with the data analysis project, which will be performed under the mentorship of the student’s master’s thesis advisor along with other collaborators preferably outside the Department of Biostatistics. In this manner the student will be given an early appreciation of the application of biostatistical techniques in real-life settings.