Northwestern University Feinberg School of Medicine
Center for Education in Health Sciences
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Competencies

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 a population’s health
D1.4 List major causes and trends of morbidity and mortality in the United States or other community relevant to the school or program
D1.5 Discuss the science of primary, secondary and tertiary prevention in population health, including health promotion and screening
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 (e.g., 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
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
PHA5. Develop a clear description of the rationale, methods, results, and overall interpretation of an epidemiologic investigation

Statistical Bioinformatics Concentration Competencies

SP1. Develop computer files of high-dimensional data for analysis using high performance computing data management techniques
SP2. Determine and execute appropriate statistical analyses, in particular techniques relevant to bioinformatics, to address a study question
SP3. Access publicly available databases for bioinformatics research
SP4. Develop statistical and bioinformatics analysis results in written, graphical and verbal format in response to an analysis request
SP5. Identify theoretical underpinnings of advanced statistical models

Statistical Methods and Practice Concentration Competencies

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
SMP5. Identify theoretical underpinnings of advanced statistical models
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