Competencies

The Master of Science in Biostatistics is offered through the Program in Public Health at Northwestern and is accredited by the Council on Education for Public Health (CEPH). In order to prepare students to pursue research in a broad range of topics, the degree requirements for the MS in Biostatistics were developed to teach students biostatistical reasoning and study design methods needed to carry out sound scientific research. Upon completion of the required coursework, all students earning the MS in Biostatistics degree should be able to do the following.

D1. Graduate-Level Professional Foundational Public Health Knowledge

• D1.1. Explain public health history, philosophy and values

• D1.2. Identify the core functions of public health and the 10 Essential Services

• D1.3. Explain the role of quantitative and qualitative methods and sciences in describing and assessing apopulation’s health

• D1.4. List major causes and trends of morbidity and mortality in the US or other community relevant to the school or program

• D1.5. Discuss the science of primary, secondary & tertiary prevention in population health, including health promotion, screening, etc

• D1.6. Explain the critical importance of evidence in advancing public health knowledge

• D1.7. Explain effects of environmental factors on a population’s health

• D1.8. Explain biological and genetic factors that affect a population’s health

• D1.9. Explain behavioral and psychological factors that affect a population’s health

• D1.10. Explain the social, political and economic determinants of health and how they contribute to population health and health inequities

• D1.11. Explain how globalization affects global burdens of disease

• D1.12. Explain an ecological perspective on the connections among human health, animal health and ecosystem health (eg, One Health)

MS in Biostatistics Competencies

• MSB1. Apply classic methods for continuous and categorical data analysis, including regression and other appropriate statistical approaches;

• MSB2. Use computer-based statistical analysis package(s) to manage data;

• MSB3. Develop visualized data using computer-based statistical analysis package(s);

• MSB4. Analyze data employing computer-based statistical analysis package(s);

• MSB5. Implement sample size and power calculations for a range of experimental designs;

• MSB6. Interpret results of a health research study, including the relation to findings from other studies, potential biological or social mechanisms, study limitations, and public health implications;

• MSB7. Communicate written and oral findings in a scientifically sound manner;

• MSB8. Calculate epidemiological measures of association between risk factors and disease;

• MSB9. Apply methods and strategies to evaluate and reduce bias in health research;

• MSB10. Use criteria to distinguish between association and causality; and

• MSB11. Apply ethical and regulatory standards to human subjects research.

Population Health Analytics Concentration Competencies

• PHA1. Design an epidemiologic study to address a question of interest;

• PHA2. Describe practical considerations for the conduct of health research studies;

• PHA3. Access publicly available data resources for population health research;

• PHA4. Critically review the scientific literature, synthesize findings across studies, and make appropriate recommendations based on current knowledge; and

• PHA5. Develop a clear description of the rationale, methods, results, and overall interpretation of an epidemiologic investigation.

• SB1. Develop computer files of high-dimensional data for analysis using high performance computing data management techniques;

• SB2. Determine and execute appropriate statistical analyses, in particular techniques relevant to bioinformatics, to address a study question;

• SB3. Access publicly available databases for bioinformatics research;

• SB4. Develop statistical and bioinformatics analysis results in written, graphical and verbal format in response to an analysis request; and

• SB5. Identify theoretical underpinnings of advanced statistical models.

• SMP1. Develop computer files of raw data for analysis using data management and statistical analysis software;

• SMP2. Execute appropriate statistical analyses to address a study question;

• SMP3. Apply classic methods for the analysis of time-to-event and clinical trial data;

• SMP4. Develop statistical analysis results in written and verbal format in response to an analysis request; and

• SMP5. Identify theoretical underpinnings of advanced statistical models.