Theory & Methods

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;

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 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;

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 introductory 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.

Course Requirements

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.

Required Courses

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.

Course # Course Name Points


Principles of Epidemiology






Data Science I



Statistical Inference



Biostatistical Methods I



Biostatistical Methods II



Capstone Consulting Seminar


Required Selectives

Students choose one selective from each group in the list below or from alternatives approved by their academic advisors. 

Group 1

Course #

Course Name



Design of Medical Experiments



Analysis of Health Surveys



Bayesian Analysis and Adaptive Designs in Clinical Trials



Introduction to Randomized Clinical Trials



Clinical Methodology



Pharmaceutical Statistics



Group 2

Course #

Course Name



Survival Analysis



Statistical Methods for Casual Inference



Graphical Models for Complex Health



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).

Course #

Course Name



Statistical Computing with SAS



Data Science II



Adv Statistical and Computational Methods in Genetics & Genomics 



Statistical Aspects of Human Population Genetics



Latent Variable and Structural Equation Modeling for Health Sciences



Topics in Advanced Statistical Computing



Research Data Coordination: Principles and Practices



Topics in Statistical Learning and Data Mining


Sample Timeline

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
P6400: Principles of Epidemiology P8109: Statistical Inference Selective/Elective P8185: Capstone Consulting Seminar
P8104: Probability P8131: Biostatistical Methods II Selective/Elective Completion of practicum requirements
P8105: Data Science I Selective/Elective Selective/Elective  
P8130: Biostatistical Methods I Selective/Elective Selective/Elective

Practicum Requirement

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.

Capstone Experience

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.


Paul McCullough
Director of Academic Programs
Department of Biostatistics
Columbia University

More information Admission Requirements.