The M.S. program trains students in theory of Biostatistics, planning, and data analysis of biomedical and public health issues, and is designed to take a minimum of two years to complete. The degree requirements and electives include biostatistics courses, statistics courses, and epidemiology or other health-related courses. The program graduates successfully compete for positions in research institutions, universities, government and other agencies.
Upon completion of the M.S. in Biostatistics, the student should be prepared to function as a statistician or statistical consultant. Therefore the student must have a very good understanding of statistical theory and practice and should be able to:
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Function as a collaborator on a research team.
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Effectively participate in the design and implementation of a health issue project.
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Assist with the design and implementation of data management systems for large health science studies.
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Prepare reports and publications resulting from health science studies.
Admission Preferences
Admission requires a B.S. degree in mathematical, biological, or physical sciences from an accredited university. The applicant’s training should include two semesters of calculus, a course in linear algebra, and at least one semester of basic probability and statistical theory courses.
Courses
The student must complete at least 24 semester hours (from required and elective courses) and expected to finish coursework within 3 semesters (1.5 years).
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Prerequisite courses1 |
Recommended Text book |
s.h.2 |
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Biostatistical Methods
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Practical Statistics for Medical Research;
Altman
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3
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Principles of Epidemiology
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Epidemiology;
Gordis
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3
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1. Could be waived upon the student’s written request and the department’s approval 2. Semester Hours.
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Required courses
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Recommended Text book
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s.h.
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Biostatistical Inference
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Statistical Inference;
Garthwaite, Jolliffe, Jones
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3
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Statistical Methods I
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Applied Linear Statistical Models;
Kutner, Nachtsheim, Neter Chapters 6-11; 22; and 25-26
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3
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Statistical Methods II
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Applied Linear Statistical Models;
Kutner, Nachtsheim, Neter Chapters 6-11; 22; and 25-26
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3
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Analysis of Categorical Data
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An Introduction to Categorical Data Analysis;
Agresti
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3
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Clinical Trials
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Fundamentals of Clinical Trials;
Friedman, Furberg, DeMets
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3
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Applied Survival Data Analysis
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Survival Analysis: A Self-Learning Text;
Kleinbaum, Klein
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3
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Elective courses* |
Recommended Text book |
s.h. |
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Statistics for Epidemiology
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Statistics for Epidemiology;
Jewell
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3
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Applied Multivariate Methods
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Applied Multivariate Statistical Analysis;
Johnson, Wichern
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3
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Introduction to Meta-Analysis
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Introduction to Meta-Analysis;
Borenstein, Hedges, Higgins, Rothstein
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3
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Statistical Learning and Data Mining
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An Introduction to Statistical Learning with …;
James; Witten; Hastie; Tibshirani
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3
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Statistical Analysis of Genetic Epidemiology Data
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A Statistical Approach to Genetic Epidemiology; Ziegler, Konig
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3
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Longitudinal Data Analysis
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Applied Longitudinal Analysis;
Fitzmaurice, Laird, Ware
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3
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Intermediate Epidemiology
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Epidemiology: Beyond the Basics;
Szklo, Nieto
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3
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Statistical Methods for Clinical Trials
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Introduction to Statistical Methods for Clinical Trials;
Cook & DeMets
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3
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Bayesian Statistics
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Applied Bayesian Statistics;
Cowles
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3
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*At least 2 courses.
M.Sc. Thesis Proposal
The thesis proposal describes the rationale for the proposed research and outlines its basic components. The proposal is submitted to the department’s research committee members (consisting of a dissertation advisor, department’s head, and department education and research representatives) for the final evaluation and approval.
Thesis Defense
The student and the thesis committee are required to comply with the School of Public Health guidelines with regard to preparation of the thesis and meeting deadlines for graduation. During the thesis defense, the thesis committee will thoroughly examine the student’s knowledge in the content area of the research.