Director: Ying Wei, PhD
The MS in Biostatistics Theory and Methods Track (MS/TM) is designed for students interested in careers as biostatisticians applying statistical methods in health-related research settings. The MS/TM Track is the appropriate program for students who intend to conclude their studies with the MS degree as well as those who want to work in the field prior to pursuing a PhD in biostatistics. It provides a stronger mathematical foundation in statistical methods than does the MPH, and can be viewed either as a terminal professional degree or as a preparatory program for those wishing to pursue further doctoral study such as a PhD in biostatistics.
All MS/TM candidates begin their studies in the fall semester. The length of the MS/TM program varies with the background, training, and experience of the candidate, but the usual period needed to complete the 36 credit MS/TM degree is two years (four semesters). In addition to fulfilling their course work, all MS/TM students also complete a one-term practicum and capstone experience.
Through a curriculum of 36 credit hours of course work, a practicum, and the capstone experience, the MS in Biostatistics Theory and Methods Track provides students with both the skills necessary for a career as a biostatistician and the background needed for doctoral study.
In addition to achieving the MS in Biostatistics core competencies, students in the Theory and Methods Track gain the following specific competencies in the areas of public health and collaborative research, data management, teaching biostatistics and biostatistical research. Upon satisfactory completion of the MS in Biostatistics Theory and Methods Track, graduates 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;
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 database 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;
Review and illustrate selected principles of study design, probability theory, estimation, hypothesis testing, and data analytic techniques to public health students enrolled in introductory level graduate public health courses; and
Apply probabilistic and statistical reasoning to structure thinking and solve a wide range of problems in public health.
MS/TM graduates are expected to master the mathematical and biostatistical concepts and techniques presented in the curriculum’s required courses. Each student's program is designed on an individual basis in consultation with a faculty advisor taking into consideration the student's prior educational experience.
Students who have mastered an academic area through previous training may have the corresponding course requirement waived. Some students, such as those with undergraduate majors in statistics or mathematics, may apply to have several courses waived. Students wishing to waive one or more courses must request approval in writing from their advisors and the Director of Academic Programs. These students must still complete a minimum of 36 points to earn the MS/TM degree.
MS/TM students are also strongly advised to develop expertise in one or more statistical software packages (such as SAS) routinely used in several of the biostatistics courses. Knowledge of one or more computer programming languages is also useful. Students without knowledge of any software packages may acquire these skills by taking “mini-courses” offered at the Computer Center in the Health Sciences Library, or by enrolling in a biostatistics course devoted to teaching the use of software packages or programming languages, such as P6110 Statistical Computing with SAS.
Below is the required course work. Students consult their faculty advisors before registering for classes to plan their programs based on their individual background, goals, and the appropriate sequencing of courses. Waiver of any required courses (with prior written approval of their faculty advisor and the Director of Academic Programs) enables students to take other, higher level classes.
Principles of Epidemiology
Data Science I
Biostatistical Methods I
Biostatistical Methods II
Capstone Consulting Seminar
Students choose one selective from each group in the list below or from alternatives approved by their academic advisors.
Design of Medical Experiments
Introduction to Randomized Clinical Trials
Analysis of Longditudinal Data
Students choose four or more courses from the list below or from alternatives approved by their academic advisors. (Please note: P8100 and P8110 are not acceptable electives for the MS/TM track).
Statistical Computing with SAS
Systematic Review & Meta Analysis
Design of Medical Experiments
Theoretical Genetic Modeling
Introduction to Randomized Clinical Trials
Randomized Clinical Trials II
Randomized Clinical Trials III: Pharmaceutical Statistics
Statistical Aspects of Human Population Genetics
Analysis of Longitudinal Data
Latent Variable and Structural Equation Modeling for Health Sciences
Topics in Advanced Statistical Computing
Research Data Coordination: Principles and Practices
Analysis of Large-Scale Data
Topics in Statistical Learning and Data Mining
Elementary Stochastic Processes
Introduction to Computer Applications in Health Care Biomedicine
Introduction to Modern Analysis
Below is a sample timeline for MS/TM candidates. Note that course schedules change from year to year, so that class days/times in future years will differ from the sample schedule below; you must check the current course schedule for each year on the course directory page.
|Fall I||Spring I||Fall II||Spring II|
|P8130-Biostatistical Methods I||P8131-Biostatistical Methods II||Elective||P8185-Capstone Consulting Seminar|
|P8104-Probability||P8109-Statistical Inference||Elective||Completion of practicum requirements|
|P8105-Data Science I||Elective||Elective|
|P6400-Principles of Epidemiology||Required Selective||Required Selective|
One term of practical experience is required of all students, providing educational opportunities that are different from and supplementary to the more academic aspects of the program. The practicum may be fulfilled during the school year or over the summer. Arrangements are made on an individual basis in consultation with faculty advisors who must approve both the proposed practicum project prior to its initiation, and the report submitted at the conclusion of the practicum experience. Students will be required to make a poster presentation at the department’s Annual Practicum Poster Symposium which is held in early May.
A formal, culminating experience for the MS degree is required for graduation. The capstone consulting seminar is designed to enable students to demonstrate their ability to integrate their academic studies with the role of biostatistical consultant/collaborator, which will comprise the major portion of their future professional practice.
As part of the seminar, students are required to attend several sessions of the Biostatistics Consulting Service (BCS). The Consultation Service offers advice on data analysis and appropriate methods of data presentation for publications, and provides design recommendations for public health and clinical research, including preparation of grant proposals. Biostatistics faculty and research staff members conduct all consultation sessions with students observing, modeling, and participating in the consultations.
In the capstone seminar, students present their experience and the statistical issues that emerged in their consultations, developing statistical report writing and presentation skills essential to their professional practice in biomedical and public health research projects.
Justine Herrera, MA
Director of Academic Programs
Department of Biostatistics
jh2477 [at] columbia.edu
More information Admission Requirements.