MS in Biostatistics Competencies

MS in Biostatistics Competencies

Biostatistics Competence
Biostatistical competency relates to the knowledge of Biostatistics methods and their application, such as descriptive statistics, inference and statistical modeling. Along with awareness of biostatistical principles, the program will inculcate in the students a critical thinking in the selection of the appropriate statistical technique (e.g., linear versus logistic regression, parametric versus semi-parametric modeling for survival data, or mixed effects versus generalized estimating equation models for longitudinal data).

The program will also build skills in the design of clinical (interventional) versus observational studies, data collection schemes and the analysis of the collected data plus interpretation and communication of the study results to public health practitioners both expert and non-expert in biostatistical methodology. A significant emphasis will be given to international issues affecting public health theory and practice as well of bioethics issues in research especially with respect to those arising in international or non-equitable settings.

Public Health Competence
Public Health competency refers to having a thorough understanding of the principles of screening and disease surveillance, prevention, observational and intervention studies, the local, national and global context of health problems, and the influence of cultural and social dimension of public health research and practice.

Computing and Data Management
The program will emphasize the appropriate methods for the design of data collection systems in the context of biomedical research (both pre-clinical and clinical, including clinical trials and observational studies), as well as the proper management, analysis and interpretation of these data.

In addition to the collection, management and analysis of biomedical data, the program will provide a solid computational background to graduating students. Instruction will be primarily in SAS (The SAS Institute, Cary, NC) and R (www.r-project.org). However, other packages (e.g., STATA) and data management packages (e.g., REDCap) will be covered. Emphasis will be given to data analysis as well as quality control and data generation (simulations).

The overarching philosophy of the MS Biostatistics program is learning by doing. This approach will culminate with the data analysis project, which will be performed under the mentorship of the student’s master’s thesis advisor along with other collaborators preferably outside the Department of Biostatistics. In this manner the student will be given an early appreciation of the application of biostatistical techniques in real-life settings.

Graduate students earning the MS in Biostatistics from the IU Richard M. Fairbanks School of Public Health will demonstrate the following Principles of Graduate and Professional Learning (PGPLs):

PGPL 1: Demonstrate knowledge and skills necessary to conduct biostatistical research.

Method of acquisition:
  • Didactic course work
  • Attendance and active participation in classes, seminars and labs
  • Direct mentoring by faculty and doctoral students
  • Participation in the writing of grant proposals and manuscripts
Assessment of learning:
  • Ability to successfully pass all required courses and qualifying examinations
  • Ability to use statistical software required of students in the program
  • Direct assessment of student progress by faculty for the master’s thesis


PGPL 2: Effectively communicate biostatistical results.

Method of acquisition:
  • Required attendance at seminars presented by faculty and peers
  • Presentations in meetings and seminars
  • Mentored writing of grant proposals and manuscripts
Assessment of learning:
  • Evaluation of oral and poster presentations in class, in seminars, and at conferences
  • Evaluation of papers and other written class assignments
  • Active participation in the writing of grants and manuscripts


PGPL 3: Think critically and creatively to solve problems in Biostatistics.

Method of acquisition:
  • Attending required seminars presented by faculty and peers
  • Solving statistical problems using SAS and other software
  • Writing pre-proposal for thesis
  • Writing thesis proposal
Assessment of learning:
  • Grades on course assignments and class presentations
  • Direct assessment by faculty on pre-proposal and thesis proposal
  • Contributions to research manuscripts


PGPL 4: Conduct biostatistical research in an ethical and responsible manner.

Method of acquisition:
  • Course content in research ethics
  • Modeling of appropriate behavior in seminars by faculty and peers
  • Direct mentoring by research director
  • Mentoring by thesis committee
Assessment of learning:
  • Grades in courses that contain research ethics content
  • Faculty observation of student’s ability to manipulate and interpret data
  • Direct oversight by thesis committee on issues of research compliance and ethics