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Master of Public Health Program
Upon
satisfactory completion of the MPH degree, all graduates will
be able to demonstrate a broad knowledge and skills base in the
core areas of public health, with particular emphasis in a selected
field of public health, and will be able to:
- Apply epidemiologic methods to the measurement
of disease rates, prevention
of infectious diseases, and the development and evaluation of
health programs and policies;
- Apply statistical methods of estimation and hypothesis
testing and explain the basics of correlation and regression
for the purpose of analyzing the health of populations;
- Analyze
how environmental contaminants (chemical, physical and other
exposures) interact with biological systems and their effect
on human populations for the purpose of evaluating risk reduction
strategies;
- Assess
the impact on health policy options of social, political, technological,
economic and cultural forces, and apply basic management techniques
to address organizational challenges to providing health care;
- Examine
public health issues and responses from a social and behavioral
sciences perspective and explain social, cultural, political,
economic, and behavioral determinants of disparities in health
status among population sub-groups; and
- Demonstrate
knowledge and skills for effective practice in their selected
field of study.
Within the
context of these overall learning objectives of the MPH program,
the Department of Biostatistics has identified additional objectives
for its students.
Master of Public (MPH)
The MPH degree
in Biostatistics (BIO) is designed to enhance the quantitative
skills of public health practitioners who use statistics frequently
in their work. This degree is intended primarily
for specialists in public health who wish to use and adapt statistical
procedures for health and medical care programs, or wish to serve
in a technical capacity as resource person and collaborators in
field and programmatic studies. Successful completion of the MPH
in Biostatistics indicates adequate preparation for the DrPH program.
Graduates of the MPH degree in BIO complete 45 credit hours
and a field practicum.
Upon satisfactory
completion of the MPH degree in BIO, graduates will be able to:
Data
Analysis and Computing
- Formulate and produce graphical displays of quantitative
information (e.g., scatter plots, box plots, line graphs) that
effectively communicate analytic findings;
- Explain general principles of study design in
attempting to identify risk factors for disease, isolate targets
for prevention, and assess the effectiveness of one or more
interventions;
- Select and perform appropriate hypothesis tests
for comparing two or more independent exposure groups, or two
or more groups of matched/clustered subjects, with respect to
a discrete or continuous response measurement of interest;
- Interpret associations estimated via linear regression,
logistic regression, and Cox models for survival data;
- Interpret quantitative findings in accurate, accessible
language for colleagues outside of biostatistics, as well as
for broader dissemination to the public and other public health
professionals;
- Apply the basic tenets of research
design and analysis for the purpose of critically reviewing
research and programs in disciplines outside of biostatistics;
- Differentiate between quantitative problems that
can be addressed with standard methods and those requiring input
from a professional biostatistician;
Public
Health and Collaborative Research
- Describe the foundations
of public health, including the biological, environmental, behavioral,
and policy factors that affect the health of populations;
- Translate research objectives into testable hypotheses;
- Compare and contrast different study designs and
their implications for inference in medical/public health research;
- Describe basic principles and the practical importance
of key concepts from probability and inference (including random
variation, systematic error, sampling error, measurement error,
hypothesis testing, type I and type II errors, confounding bias,
and effect modification) to colleagues without extensive statistical
training;
- Develop and execute power and sample size calculations
for research studies utilizing simple random sampling;
- Formulate and prepare written plans
for statistical analysis of research data from medicine and
public health that clearly reflect the research hypotheses of
the proposal in a manner that resonates with both co-investigators
and grant reviewers;
Teaching
Biostatistics
- Review and illustrate selected
principles of study design, probability theory, estimation,
hypothesis testing, and data analytic techniques to public health
students enrolled in first and second level graduate public
health courses; and
Biostatistical
Research
- Apply probabilistic and statistical
reasoning to structure thinking and solve a wide range of problems
in public health.
Master of Science (MS)
Students
in the MS degree in BIO degree program select one of three tracks
of specialization: Theory and Methods, Clinical Research Methods,
and Patient Oriented Research.
The three specialty tracks include the core MS degree in
BIO learning objectives plus additional objectives specific to
each area of emphasis. The Theory and Methods track is designed
for the student interested in a career as a biostatistician. It
provides sufficient preparation for students who want to pursue
doctoral degrees in biostatistics. The Clinical Research Methods
track provides formal, rigorous training in the design and analysis
of clinical research studies. It is designed for physicians, nurses,
dentists, pharmacists, and other health care professionals with
extensive clinical research experience who require superior skills
in applied statistics in order to better pursue research in their
own fields of expertise. The Patient Oriented Research track is
a broadly based didactic training program that prepares young
investigators for independent careers as clinical scientists.
In contrast with the Clinical Research Methods track, all candidates
in the Patient Oriented Research track must have completed a doctorate
in a clinical discipline prior to enrollment. This program is
further distinguished from the Clinical Research Methods track
in that it is supported by a K30
- Clinical Research Curriculum Development Award from the NIH.
The Patient Oriented Research track offers 6 scholarships per
year to exceptional applicants.
Upon
satisfactory completion of the MS degree in BIO, graduates will
be able to:
Data
Analysis and Computing
- Formulate and produce graphical displays of quantitative
information (e.g., scatter plots, box plots, line graphs) that
effectively communicate analytic findings;
- Explain general principles of study design in
attempting to identify risk factors for disease, isolate targets
for prevention, and assess the effectiveness of one or more
interventions;
- Select and perform appropriate hypothesis tests
for comparing two or more independent exposure groups, or two
or more groups of matched/clustered subjects, with respect to
a discrete or continuous response measurement of interest;
- Interpret associations estimated via linear regression,
logistic regression, and Cox models for survival data;
- Apply the basic tenets of research
design and analysis for the purpose of critically reviewing
research and programs in disciplines outside of biostatistics;
- Interpret quantitative findings in accurate, accessible
language for colleagues outside of biostatistics, as well as
for broader dissemination to the public and other public health
professionals;
Public Health and Collaborative Research
- Translate research objectives into testable hypotheses;
- Compare and contrast different study designs and
their implications for inference in medical/public health research;
- Describe basic principles and the practical importance
of key concepts from probability and inference (including random
variation, systematic error, sampling error, measurement error,
hypothesis testing, type I and type II errors, confounding bias,
and effect modification) to colleagues without extensive statistical
training;
- Develop and execute power and sample size calculations
for research studies utilizing simple random sampling; and
- Evaluate research reports and proposals for research
funding on the basis of their scientific integrity, validity,
and the strength of the quantitative analysis.
Theory
and Methods - Track
Specific Learning Objectives
Graduates of this track will be able to:
Public
Health and Collaborative Research
- Develop and execute calculations for power and
sample size when planning research studies with complex sampling
schemes;
- Formulate and prepare a written statistical plan
for analysis of public health research data that clearly reflects
the research hypotheses of the proposal in a manner that resonates
with both co-investigators and peer reviewers;
- Prepare written summaries of quantitative analyses
for journal publication, presentations at scientific meetings,
grant applications, and review by regulatory agencies;
Data
Management
- Identify the uses to which data management can
be put in practical statistical analysis, including the establishment
of standards for documentation, archiving, auditing, and confidentiality;
guidelines for accessibility; security; structural issues; and
data cleaning;
- Differentiate between analytical and data management
functions through knowledge of the role and functions of databases,
different types of data storage, and the advantages and limitations
of rigorous data base systems in conjunction with statistical
tools;
- Describe the different types of database management
systems, the ways these systems can provide data for analysis
and interact with statistical software, and methods for evaluating
technologies pertinent to both;
- Assess database tools and the database functions
of statistical software, with a view to explaining the impact
of data management processes and procedures on their own research;
Teaching
Biostatistics
- Review and illustrate selected
principles of study design, probability theory, estimation,
hypothesis testing, and data analytic techniques to public health
students enrolled in first and second level graduate public
health courses; and
Biostatistical
Research
- Apply probabilistic and statistical reasoning
to structure thinking and solve a wide range of problems in
public health.
Clinical
Research Methods - Track
Specific Learning Objectives
Graduates of this track will be able to:
Data
Analysis and Computing
- Apply the basic tenets of research design and
analysis for the purpose of critically reviewing research and
programs in disciplines outside of biostatistics;
- Differentiate between quantitative problems that
can be addressed with standard methods and those requiring input
from a professional biostatistician.
Public
Health and Collaborative Research
- Formulate and prepare a written statistical plan
for analysis of public health research data that clearly reflects
the research hypotheses of the proposal in a manner that resonates
with both co-investigators and peer reviewers;
- Prepare written summaries of quantitative analyses
for journal publication, presentations at scientific meetings,
grant applications, and review by regulatory agencies;
Data
Management
- Identify the uses to which data management can
be put in practical statistical analysis, including the establishment
of standards for documentation, archiving, auditing, and confidentiality;
guidelines for accessibility; security; structural issues; and
data cleaning;
- Differentiate between analytical and data management
functions through knowledge of the role and functions of databases,
different types of data storage, and the advantages and limitations
of rigorous data base systems in conjunction with statistical
tools;
- Describe the different types of database management
systems, the ways these systems can provide data for analysis
and interact with statistical software, and methods for evaluating
technologies pertinent to both; and
- Assess database tools and the database functions
of statistical software, with a view to explaining the impact
of data management processes and procedures on their own research;
Patient
Oriented Research - Track
Specific Learning Objectives
Graduates of this track will be able to:
Data
Analysis and Computing
- Apply the basic tenets of research design and
analysis for the purpose of critically reviewing research and
programs in disciplines outside of biostatistics;
- Differentiate between quantitative problems that
can be addressed with standard methods and those requiring input
from a professional biostatistician; and
Public
Health and Collaborative Research
- Discuss basic laboratory methods commonly used
in patient oriented research.
Doctor of Public Health (DrPH)
The DrPH
degree in BIO is designed for persons who wish to apply state-of-the-art
statistical methods to the solution of important public health
problems.
In addition
to meeting the learning objectives of the MPH in BIO, graduates
of the DrPH degree in BIO will be able to:
Data Analysis and Computing
- Identify and implement advanced statistical models
for the purposes of estimation, comparison, prediction, and
adjustment in non-standard settings;
Public Health and Collaborative Research
- Describe the foundations of
public health, including the biological, environmental, behavioral,
and policy factors that affect the health of populations;
- Develop and execute calculations
for power and sample size when planning research studies with
complex sampling schemes;
- Formulate and prepare a written statistical plan
for analysis of public health research data that clearly reflects
the research hypotheses of the proposal in a manner that resonates
with both co-investigators and peer reviewers;
- Evaluate research reports and proposals for research
funding on the basis of their scientific integrity, validity,
and the strength of the quantitative analysis;
- Prepare written summaries of quantitative analyses
for journal publication, presentations at scientific meetings,
grant applications, and review by regulatory agencies;
Data
Management
- Identify the uses to which data management can
be put in practical statistical analysis, including the establishment
of standards for documentation, archiving, auditing, and confidentiality;
guidelines for accessibility; security; structural issues; and
data cleaning;
- Differentiate between analytical and data management
functions through knowledge of the role and functions of databases,
different types of data storage, and the advantages and limitations
of rigorous data base systems in conjunction with statistical
tools;
- Describe the different types of database management
systems, the ways these systems can provide data for analysis
and interact with statistical software, and methods for evaluating
technologies pertinent to both;
- Assess database tools and the database functions
of statistical software, with a view to explaining the impact
of data management processes and procedures on their own research;
Teaching
- Review and illustrate selected principles of study
design, probability theory, estimation, hypothesis testing,
and data analytic techniques to public health students enrolled
in first and second level graduate public health courses;
- Explain advanced concepts in the theory of statistical
inference to graduate students in biostatistics and mathematical
statistics;
Biostatistical Research
- Identify and integrate new developments in the
statistical literature for challenging research problems in
public health; and
- Generate original computer code for new statistical
techniques.
Doctor of Philosophy (PhD)
The program
requirements for the PhD degree in BIO differ from those for the
DrPH in that the curriculum, examinations and dissertation involve
more emphasis on statistical theory in the context of public health
applications.
In addition
to meeting the learning objectives of the MS Theory and Methods
track, graduates of the PhD program in BIO will be able to:
Data Analysis and Computing
- Identify and implement advanced statistical models
for the purposes of estimation, comparison, prediction, and
adjustment in non-standard settings;
Public Health and Collaborative Research
- Develop and execute calculations
for power and sample size when planning research studies with
complex sampling schemes;
- Formulate and prepare a written statistical plan
for analysis of public health research data that clearly reflects
the research hypotheses of the proposal in a manner that resonates
with both co-investigators and peer reviewers;
- Evaluate research reports and proposals for research
funding on the basis of their scientific integrity, validity,
and the strength of the quantitative analysis;
- Prepare written summaries of quantitative analyses
for journal publication, presentations at scientific meetings,
grant applications, and review by regulatory agencies;
Data
Management
- Identify the uses to which data management can
be put in practical statistical analysis, including the establishment
of standards for documentation, archiving, auditing, and confidentiality;
guidelines for accessibility; security; structural issues; and
data cleaning;
- Differentiate between analytical and data management
functions through knowledge of the role and functions of databases,
different types of data storage, and the advantages and limitations
of rigorous data base systems in conjunction with statistical
tools;
- Describe the different types of database management
systems, the ways these systems can provide data for analysis
and interact with statistical software, and methods for evaluating
technologies pertinent to both;
- Assess database tools and the database functions
of statistical software, with a view to explaining the impact
of data management processes and procedures on their own research;
Teaching
- Review and illustrate selected principles of study
design, probability theory, estimation, hypothesis testing,
and data analytic techniques to public health students enrolled
in first and second level graduate public health courses;
- Explain advanced concepts in the theory of statistical
inference to graduate students in biostatistics and mathematical
statistics;
Biostatistical Research
- Identify and integrate new developments in the
statistical literature for challenging research problems in
public health;
- Generate original computer code for new statistical
techniques;
- Recognize gaps in current inferential methods
that limit further public health research and propose solutions
based on rigorous theoretical justification; and
- Develop guidelines for practical
implementation and evaluation of public health research and
programs.
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