Master of Science in Biostatistics
The Master of Science in Biostatistics program comprises 30 credit hours across 12 courses including electives in various areas of interest.
|Courses Code||Course Title||Credit Hours|
|PHST 680||Biostatistical Methods I||3|
|PHST 624||Clinical Trials I||2|
|PHPH 523||Public Health in the U.S.1||(2)|
|PHST 662||Mathematical Statistics||3|
|PHST 681||Biostatistical Methods II||3|
|PHST 684||Categorical Data Analysis||3|
|PHST 625||Clinical Trials II||2|
|PHST 683||Survival Analysis||3|
|Degree Total||30 (32)|
- PHPH 523 fulfills the accreditation requirement that all graduates from the School of Public Health and Information Science receive foundational instruction in public health. The two credit hours for PHPH 523 do not accrue toward the 30 hours required for the MS degree completion. Students with a prior degree and/or coursework in a public health field or substantial experience in the public health workforce may be relieved of this requirement, per approval of the Associate Dean for Academic Affairs.
- Electives are chosen with the approval of a faculty advisor. Students are typically encouraged to select electives from among the following courses offered by the Department of Bioinformatics and Biostatistics:
- PHST 603 Biostatistics Public Health Practicum I
- PHST 620 Introduction to Statistical Computing
- PHST 675 Independent Study in Biostatistics
- PHST 682 Multivariate Statistics
Students may also choose elective courses outside of the department in fields related to biostatistics, such as Mathematics, Epidemiology, and Computer Science (subject to approval from faculty advisor). Students are responsible for ensuring they have met the prerequisites for these courses.
Sample Course Descriptions
PHST 661 Probability
Prerequisite: Enrolled in BDSCMS BIO or Math major in Graduate School. Introduction to probability theory. Topics include axioms of probability, conditional probability, discrete and continuous random variables, probability distributions and joint distributions, moments, moment generating functions, mathematical expectation and transformation of random variables, limit theorems (Law of Large Numbers and Central Limit Theory).
PHST 680 Biostatistical Methods I
Prerequisite: Enrolled in BDSCMS BIO, PH MPH and PHST 501, Math major in Graduate School. A mathematically sophisticated presentation of statistical principles and methods. Topics include exploratory data analysis, graphical methods, point and interval estimation, hypothesis testing, and categorical data analysis Matrix algebra is required. Data sets drawn from biomedical and public health literature will be analyzed using statistical computer packages.
PHST 624 Clinical Trials I
Prerequisite: Enrolled in BDSCMS BIO, CISCCCI, CISCMSC, MD/MSc. Phases of Trials, Ethical Issues, Basic Design, Inclusion and Exclusion criteria, Randomization and Blinding, Sample Size, Monitoring Response Variables, and Issues in Data Analysis
PHST 662 Mathematical Statistics
Prerequisite: Enrolled in BDSCMS BIO, Math major in Graduate School and PHST 661. A first course in statistical theory. Topics include limiting distributions, maximum likelihood estimation, least squares, sufficiency and completeness, confidence intervals, Bayesian estimation, Neyman-Pearson Lemma, uniformly most powerful tests, likelihood ratio tests and asymptotic distributions.
PHST 681 Biostatistical Methods II
Prerequisite: Enrolled in BDSCMS BIO, PH MPH, Math major in Graduate School and PHST 680. This course offers a mathematically sophisticated introduction to simple regression models and analysis of variance. Matrix algebra is required and data analysis will be illustrated drawing examples from biomedical and public health literature.
PHST 684 Categorical Data Analysis
Prerequisite: Enrolled in BDSCMS BIO, PH MPH and PHST 501. Topics include inference for two-way contingency tables, models for binary response variables, log-linear models, models for ordinal data, multinomial response data, Poisson regression and analysis of repeated categorical response data. Emphasis will be placed on methods and models most useful in biomedical and public health research.
PHST 625 Clinical Trials II
Prerequisite: PHST 624 and enrolled in BDSCMS BIO, CISCCCI, CISMSC, MD/MSc. Sample Size and Power Analysis, Survival Analysis, Sequential Design, Meta Analysis, Reporting and Interpreting of Results, Multicenter Trails. SPSS will be used.
PHST 683 Survival Analysis
Prerequisite: Enrolled in BDSCMS BIO, PH MPH and PHST 681. Statistical methods for analyzing survival data. Parametric and nonparametric methods for complete and incomplete data, life-table, KM estimator, accelerated lifetime models, proportional hazard models, log-rank tests, and goodness-of-fit tests.
PHST 620 Introduction to Statistical Computing
Prerequisite: Enrolled in PH MPH and PHST 500. This course provides an introduction to SAS. It will give students an overview of the SAS system under MS Windows and provide fundamental grounding in the environment for accessing, structuring, formatting and manipulating data. Students will learn how to summarize and display data, and the inference between data steps and procedures to get information out of data.
PHST 682 Multivariate Statistics
Prerequisite: Enrolled in BDSCMS BIO, Math major in Graduate School and PHST 681. The topics covered in this course include the multivariate normal distribution, inference for mean vectors, inference for covariance and correlation matrices, analysis of covariance structure, analysis of serial measurements, factor analysis, and discriminant analysis. Statistical methods and models that are most useful in multivariate data analysis will be introduced. Instruction will also be given in the proper use of R to carry out these analyses.