Biostatistics

Prospective students should click here to obtain specific admissions requirements on how to apply to Graduate School.


Degree Offered: M.P.H., M.S., M.S.P.H., Ph.D.
Department Chair: David T. Redden, Ph.D.
Phone: (205) 934-4905
E-mail: bstgrad@uab.edu
Website: www.soph.uab.edu/bst
Department Contact: Della Daniels
Department Contact Email: ddaniel@uab.edu

The Department of Biostatistics at the University of Alabama at Birmingham (UAB) is one of five departments in the School of Public Health: Biostatistics, Environmental  Health  Science, Epidemiology, Health Behavior, and Health Care Organization and Policy.

Dr. David Redden is the Chair of the department, Dr. Jeff M Szychowski is the Director of Graduate Studies, and Ms. Della Daniel is the department liaison to the graduate program. The department currently has 17 faculty members, 48 full-time staff, and is organized into two sections: 1) The Section on Statistical Genetics (SSG) and 2) The Section on Research Methods and Clinical Trials (RMCT). Members of the department conduct research in statistical methodology and applications, as well as in fundamental problems of modeling in biological systems. Much of the department’s research is collaborative in nature involving projects from basic science, genetics, clinical medicine, public health, and other health-related areas, both within and outside of UAB. Grant support for faculty in the department fall into four broad areas: 1) applied grants involving the application of statistical methods to health-related issues, 2) statistical coordinating centers for large multi-center randomized clinical trials, 3) methodological grants advancing statistical techniques, and 4) training grants for preparing the next generation of statisticians.

Degree Programs

The Department offers programs leading to the Doctor of Philosophy (PhD), Master of Science (MS), Master of Public Health (MPH), Master of Science in Public Health (MSPH), and a Certificate in Statistical Genetics (CSG). The MS and PhD degrees are offered through the Graduate School. The MPH and MSPH degrees are offered through the School of Public Health.

Biostatistics Degree Competencies - click here


Admissions

Entry Term Deadline
Master Program Deadline: www.soph.uab.edu/apply
PhD Program Deadline: May 1
GPA 3.0
Number of Evaluation Forms Required: Three
Entrance Tests: GRE www.soph.uab.edu/prospective/testing
TOEFL TOEFL is required for international applicants whose native language is not English.
SOPH Graduate Catalog: www.soph.uab.edu/catalog
SOPH Admissions: www.soph.uab.edu/graduate

School of Public Health's Student Catalog

Master of Science in Biostatistics

The Department of Biostatistics offers an MS degree in Biostatistics. This program provides a balance between theory and application, the perspective being the role of statistics and modeling in scientific research. The objective is to produce research-oriented scientists who can advance statistical and modeling theory and can interact effectively with scientists in other disciplines to advance knowledge in those fields. For admission to the MS program, a student's undergraduate curriculum must include a 3-semester sequence of calculus or equivalent, linear matrix algebra, and proficiency in computing. It is preferred that students have additional advanced mathematics courses, e.g., differential equations, advanced calculus including special functions, and complex analysis. Some background in the natural sciences would be helpful. Interested students should contact the Department of Biostatistics.

MS Comprehensive Exam

Upon completion of the first year-and-a-half of course work, the candidate is given a written examination consisting of two parts - Applied Statistics and Theory of Statistics. The exam will test the students on their understanding and comprehension of the foundation of the theory and applications of statistics, and will generally cover materials from BST 621, BST 622, BST 623 BST 626, BST 631, BST 632 and BST 655. This will be a standard departmental exam, administered by the GPC. The criteria for evaluation are the candidate’s understanding and competency in basic principles and foundations of statistics, understanding of the appropriate use and interpretation of statistical methods, and ability to succinctly express in writing the results of the problems. This examination is offered during the first half of January. At first attempt, a student must take both parts at the same time. For those years during which at least one student needs to take the exam a second time, the exam may be offered in July at the discretion of the Graduate Program Committee (GPC). Students must be registered for at least 3 semester hours of graduate work during the semester in which the comprehensive examination is given.

The student must pass each part of the exam at the Masters level. If a student fails either part of the exam, one additional chance will be given to retake the part of the exam that was failed. A student who fails the qualifying exam more than once will be dismissed from the MS program. The student has the opportunity to appeal the decision of his/her dismissal. The Graduate School policies on dismissal from the program and appeal of dismissal are described in detail in the UAB Student Handbook.

Please note that receipt of an “A” in all individual courses may not constitute adequate preparation for this exam. The purpose of the qualifying exam is to test your ability to connect the information across courses, to choose appropriate analysis methods, and to display a working knowledge of the tools used in probability and inference.

Masters Project

Immediately after passing the MS Comprehensive examination, the student must form a research project committee consisting of at least 3 members, chaired by the research advisor. Upon successful completion of the project, the student must submit a final write-up of the research and present their work orally in a departmental seminar. It is strongly suggested that the write-up is such that it may lead to an article submitted for publication in the subject matter area. The date and time of the oral presentation will be advertised in the Ryals Building.
All students must be registered for a minimum of 3 credit hours of Non-Thesis Research BST 698 during the semester in which you intend to graduate. When you are nearing completion of your research, you must file an Application for Degree with the Graduate School by the appropriate date during the semester in which you expect to graduate.

RequirementsHours
MS Required Courses:27
BST 621Statistical Methods I3
BST 622Statistical Methods II3
BST 623General Linear Models3
BST 626Data Management and Reporting with SAS3
BST 626LData Management and Reporting with SAS Laboratory0
BST 631Statistical Theory I4
BST 632Statistical Theory II4
BST 655Categorical Data Analysis3
BST 691Pre-Doctoral Seminar Series (4 hours required for BST 691: Biostatistics Pre‐doctoral Seminar Series)1
SOPH Requirement:3
GRD ESL Assessment3
Biostatistics Electives: (Minimum 6 credit hours)6
BST 665Survival Analysis3
BST Course3
Required Outside Electives: (Minimum 7 graduate credit hours of electives)7
EPI 610Principles of Epidemiologic Research4
Other Elective3
Other Related Courses including BST 698: (Minimum of 6 credit hours)6
BST 698Non Thesis Research1-12
Total Hours:46-49

School of Public Health's Student Catalog

Master of Public Health in Biostatistics

The MPH degree in Biostatistics is intended primarily for those who wish to acquire an MPH degree with an emphasis on statistical methodology. This can include individuals from decision-making positions in health care settings as well as those interested in data management, statistical analyses and interpretation, and presentation of analytical results. This degree can be completed in approximately 2 years. Note that the MPH does not require some of the theoretical courses required for the MS, and as such, it is not a direct route to prepare a student for a PhD. Students anticipating that they will wish to continue for a PhD in biostatistics are advised to pursue the MS rather than the MPH.

All international students must demonstrate proficiency in spoken and written English before graduation, through the Graduate School’s ESL Assessment. Dependent on the results of that assessment, the Graduate Program Committee may require additional course work in both written and/or oral English for students  not  showing  proficiency  upon  arrival,  or  during  any  period  of  their  graduate  studies.

RequirementsHours
BST 621Statistical Methods I3
BST 622Statistical Methods II3
ENH 600Fundamentals of Environmental Health Science3
EPI 600Intro to Epidemiology3-4
or EPI 610 Principles of Epidemiologic Research
HB 600Social and Behavioral Science Core3
HCO 600Management and Policy in Public Health Systems and Services3
PUH 695The Public Health Integrative Experience1
SOPH Requirements:
PUH 627Writing & Reviewing Research for MPH Candidates3
or GRD 727 Writing & Reviewing Research
Biostatistics Track Requirements:
BST 619Data Collection and Management3
BST 626Data Management and Reporting with SAS3
BST 626LData Management and Reporting with SAS Laboratory0
Biostatistics Electives (Minimum 9 credit hours of regular courses of 623 or higher level)9
Public Health Electives at the 500+level 13
Internship
BST 697Internship in Biostatistics3
Total Hours43-44
1

 Please choose three (3) hours at the 500+ level from ENH, EPI, GHS, HB, or HCO

School of Public Health's Catalog

Master of Science in Public Health (MSPH) in Clinical and Translational Science (CTSB)

There is a growing interest in medical and other health science schools in developing the clinical research skills of faculty members and fellows. This interest has been fueled by increased support from the National Institutes of Health (NIH) to prepare such individuals to meet the demand for clinical investigators in the field. Locally, the Schools of Medicine and Public Health have combined efforts to create a training program for young faculty members and fellows from a variety of disciplines.

This program is a post-medical or other health science degree training program, aimed primarily at fellows and faculty members interested in developing skills required for clinical research. It is anticipated that this academic training will supplement extensive training in the content area in which the student is trained, and senior mentoring in the politics and policies of development and management. A graduate of this program will have the academic training to develop and lead independent research programs and projects. The program consists of a set of courses common to all students, plus research electives and focus elective courses that reflect the academic interest of the student. At this time, the program can accommodate students with specific interest in biostatistics (CTSB), epidemiology (CTE), and health behavior (CTSH).  As a result, there will be some variation in the specific knowledge and skills acquired by each graduate. However, the primary learning objectives will apply to all students, irrespective of departmental affiliation. As such, graduates will be able to do the following upon completion of the program:

  • design, conduct, and evaluate clinical research studies;
  • understand issues of data collection and study management;
  • follow appropriate policies and procedures relating to the utilization of human subjects in clinical research;
  • demonstrate an understanding of the ethics of research on human subjects;
  • prepare competitive applications for extramural research funding;
  • prepare manuscripts for publication in the scientific literature; and
  • critically evaluate published research.

Required Courses: MSPH in Biostatistics

The MSPH in Clinical and Translational Science consists of a minimum of 41 credit hours. Of these, 14 hours are required, including 9 hours of specific biostatistics courses and 5 hours of specific epidemiology courses. Students then select at least 9 hours from a list of approved Masters Research Electives, complete 9 hours of focus specific electives in biostatistics, and take at least 9 hours of directed (698 level) masters research to fulfill the MSPH requirement for conducting a research project.

Students receiving a MSPH are required to complete a 3 hour Online course entitled "Overview of Public Health" by the end of their second semester. Students with prior public health education (coursework in each of the public health core disciplines) may be waived from this requirement by permission of the Associate Dean. 

All international students must demonstrate proficiency in spoken and written English before graduation, through the Graduate School’s ESL Assessment. Dependent on the results of that assessment, the GPC may require additional course work in both written and/or oral English for students not showing proficiency upon arrival, or during any period of their graduate studies.

The MSPH Research Project

The student, with the advice of his/her chosen MSPH project co-directors forms a small committee (minimum 3 members) to guide the research project. The committee co-chairs should consist of a faculty member from Biostatistics and an MD with experience in the area of clinical research. Upon successful completion of the project, the student must submit a final write-up of the research.

RequirementsHours
MSPH Core Requirement: 14
BST 621Statistical Methods I3
BST 622Statistical Methods II3
BST 625Design/Conduct Clinical Trials3
EPI 607Fundamentals of Clinical Research3
EPI 680Topics in Clinical Research2
SOPH Requirement:3
GRD ESL Assessment:3
Masters Research Selectives: Choose from list below (Minimum 9 credit hours taken from the following courses)9
BST 619Data Collection and Management3
BST 626Data Management and Reporting with SAS3
BST 626LData Management and Reporting with SAS Laboratory0
ENH 650Essentials of Environmental and Occupational Toxicology and Diseases3
EPI 625Quantitative Methods in Epidemiology3
EPI 703Special Topics in the Epidemiology of Chronic Disease3
HB 624Advanced Social and Behavioral Science Theory3
HCO 677Patient-Based Outcomes Measurement3
Biostatistics Selectives: (Minimum 9 hours of regular courses of 623 or higher level)9
Masters Project Research: (9 hours of supervised research in clinical setting)9
BST 698Non Thesis Research1-12
Total Credit Hours: 41 - 44 hours

School of Public Health's Student Catalog

Doctor of Philosophy in Biostatistics

The Department of Biostatistics offers a PhD degree in biostatistics. This program provides a balance between theory and application, the perspective being the role of statistics and modeling in scientific research. The objective is to produce research-oriented scientists who can advance statistical and modeling theory and can interact effectively with scientists in other disciplines to advance knowledge in those fields. For admission to the program, a student's undergraduate curriculum must include a 3-semester sequence of calculus or equivalent, linear algebra, and proficiency in computing. It is preferred that students have additional advanced mathematics courses, e.g., differential equations, advanced calculus including special functions, and complex analysis. Advanced calculus and a prior MS in statistics or biostatistics are required for admission to the PhD program. Some background in the natural sciences would be helpful. Interested students should contact the department of Biostatistics.

All students entering the PhD program are required to complete the coursework required for the MS degree. As described above, it is possible for a student entering the graduate program with an MS degree in statistics or biostatistics from another institution to waive up to 12 credit hours of coursework at the discretion of the GPC. It will be the student’s option whether to actually obtain the MS degree, but the department strongly encourages that they do so, since the completion of the master’s project is very good research experience and may lead to a publication.

PhD Qualifying/Comprehensive Exam

Upon completion of the first year-and-a-half of course work, the candidate is given a written examination consisting of two parts - Applied Statistics and Theory of Statistics. The exam will test the student on their understanding and comprehension of the foundation of the theory and applications of statistics, and will generally cover materials from BST 621, BST 622BST 623, BST 626, BST 631, BST 632 and BST 665. This will be a standard departmental exam, administered by the GPC. The criteria for evaluation are the candidate’s understanding and competency in basic principles and foundations of biostatistics, potential for conducting independent research in statistical methods, and ability to express in writing the results of the problems. This examination is offered during the first half of January. At first attempt, a student must take both parts at the same time. For those years during which at least three students need to take the exam a second time, the exam may be offered in July at the discretion of the GPC. Students must be registered for at least 3 semester hours of graduate work during the semester in which the comprehensive examination is given.

The student may pass each part of the exam at the PhD level, fail at the PhD level but pass at the Master’s level, or fail at the Masters level. If a student fails to pass either part of the exam at the PhD level, one additional chance will be given to retake the part of the exam that was failed. A student who fails the qualifying examination more than once will be dismissed from the PhD program. The student has the opportunity to appeal the decision of his/her dismissal. Graduate School policies on dismissal from the program and appeal of dismissal are described in detail in the UAB Student Handbook.

Please note that receipt of an “A” in all individual courses may not constitute adequate preparation for this exam. The purpose of the qualifying exam is to test the students’ ability to connect the information across courses, to choose appropriate analysis methods, and to display a working knowledge of the tools used in probability and inference.  It is highly recommended that students find a mentor within six months after successfully completing the qualifying examination.

PhD Dissertation Research

The student should start his/her dissertation research during the second or third year of study. The initial step of the research consists of identifying a topic that is of mutual interest to the student and the research advisor. Courses, seminars, and presentations by the faculty assist the student in this process. The dissertation must be an original contribution to scientific knowledge.  It can involve, but is not limited to, the development of new statistical methodologies, evaluation of existing methodologies and study of their properties, innovative application of existing methodologies, or any combination of the above. It must show a clear ability to carry out independent biostatistical research and provide results that are publishable in peer-reviewed journals.

RequirementsHours
Required Biostatistics Courses:45
BST 621Statistical Methods I3
BST 622Statistical Methods II3
BST 623General Linear Models3
BST 626Data Management and Reporting with SAS3
BST 626LData Management and Reporting with SAS Laboratory0
BST 631Statistical Theory I4
BST 632Statistical Theory II4
BST 665Survival Analysis3
BST 691Pre-Doctoral Seminar Series (Minimum of 6 hours)1
BST 723Theory of Linear Models3
BST 735Advanced Inference4
BST 760Generalized Linear and Mixed Models3
BST 765Advanced Computational Methods3
SOPH Requirement: 3
GRD ESL Assessment3
Biostatistics Electives: 29
Required Public Health/Medical/Biological Electives: 37
EPI 610Principles of Epidemiologic Research4
Other Elective3
Other Related Courses including Research in Statistics:3
BST 798Non-Dissertation Research1-12
Dissertation Research24
BST 799Dissertation Research1-12
Total Hours85-88
1

Other Related Courses including Dissertation Research (BST 799).  Two semesters in candidacy and either (1) 24 credit hours of BST 799 or (2) 12 credit hours of research-based coursework (approved by Program Director).

2

Minimum 9 credit hours of 700 level courses

3

Minimum of 7 hours

Courses

BST 601. Biostatistics. 4 Hours.

Logic and language of scientific methods in life science research; use of basic statistics in testing hypotheses and setting confidence limits. Simple and multiple regression and elementary experimental designs. No prerequisites but a familiarity with basic algebra is important.

BST 601Q. Biostatistics. 4 Hours.

Logic and language of scientific methods in life science research; use of basic statistics in testing hypotheses and setting confidence limits. Simple and multiple regression and elementary experimental designs. No prerequisites but familiarity with basic algebra is important.

BST 603. Introductory Biostatistics for Graduate Biomedical Sciences. 3 Hours.

This course will utilize current statistical techniques to assess and analyze health science related data.

BST 607. Environmental Sampling and Exposure Assessment. 3 Hours.

Application of statistical techniques including use of lognormal distribution for environmental and occupational health exposure assessment problems. Spatial and temporal correlations are discussed and appropriate analysis techniques are described for these situations using statistical software packages.

BST 608. Statistical Modeling in Clinical and Epi Studies. 3 Hours.

Provide an understanding of modeling approaches to address the challenges of "real Life" data sets in the framework of linear models as they relate to clinical and epidemiological studies.
Prerequisites: BST 602 [Min Grade: C] and BST 612 [Min Grade: C]

BST 611. Intermediate Statistical Analysis I. 3 Hours.

Students will gain a thorough understanding of basic analysis methods, elementary concepts, statistical models and applications of probability, commonly used sampling distributions, parametric and non-parametric one and two sample tests, confidence intervals, applications of analysis of two-way contingency table data, simple linear regression, and simple analysis of variance. Students are taught to conduct the relevant analysis using current software such as the Statistical Analysis System (SAS).

BST 611Q. Intermediate Statistical Analysis I Online. 3 Hours.

This course will utilize current statistical techniques to assess and analyze public health related data. In addition, students will learn to read and critique the use of such techniques in published research. Students will also determine what analytical approaches are appropriate under different research scenarios.

BST 612. Intermediate Statistical Analysis II. 3 Hours.

This course will introduce students to the basic principles of tools of simple and multiple regression. A major goals is to establish a firm foundation in the discipline upon which the applications of statistical and epidemiologic inference will be built. If prerequisite is not met, permission of instructor is required.
Prerequisites: BST 611 [Min Grade: C]

BST 612Q. Intermediate Statistical Analysis II Online. 3 Hours.

This course will utilize current statistical techniques to assess and analyze public health related data. In addition, students will learn to read and critique the use of such techniques in published research. Students will also determine what analytical approaches are appropriate under different research scenarios.
Prerequisites: BST 611 [Min Grade: C]

BST 613. Intermediate Statis Analy III. 3 Hours.

This course will introduce students to additional general concepts in biostatistics beyond an introductory level to include study design, power and sample size estimation, mixed-models, survival analysis, survey design and interpretation of research results.
Prerequisites: BST 612 [Min Grade: C]

BST 619. Data Collection and Management. 3 Hours.

Basic concepts of study design, forms design, quality control, data entry, data management and data analysis. Hands-on experience with data entry systems, e.g., DBASE, and data analysis software, e.g., PC-SAS. Exposure to other software packages as time permits. Previous computer experience or workshop on microcomputers highly recommended. NOTE: If space permits, non-degree graduate students will be permitted to enroll. All students registered for the course must attend 1st class to remain enrolled. Previous computer experience or workshop on microcomputers highly recommended.
Prerequisites: BST 601 [Min Grade: C] or BST 611 [Min Grade: C] or BST 621 [Min Grade: C]

BST 620. Applied Matrix Analysis. 3 Hours.

Vector and matrix definitions and fundamental concepts; matrix factorization and application. Eigen-values and eigen-vectors, functions of matrices, singular and ill-conditioned problems.
Prerequisites: BST 622 [Min Grade: C]

BST 621. Statistical Methods I. 3 Hours.

Mathematically rigorous coverage of applications of statistical techniques designed for Biostatistics majors and others with sufficient mathematical background. Statistical models and applications of probability; commonly used sampling distributions; parametric and nonparametric one and two sample tests and confidence intervals; analysis of two-way contingency table data; simple linear regression; simple analysis of variance designs with equal or proportional subclass members; use of contrasts and multiple comparisons procedures; introduction to survival analysis; multivariate methods. Interested students must have a year of calculus sequence before enrolling in BST 621.

BST 622. Statistical Methods II. 3 Hours.

Mathematically rigorous coverage of applications of statistical techniques designed for Biostatistics majors and others with sufficient mathematical background. Statistical models and applications of probability; commonly used sampling distributions; parametric and nonparametric one and two sample tests and confidence intervals; analysis of contingency tables; simple linear regression; simple analysis of variance designs with equal or proportional subclass members; use of contrasts and multiple comparisons procedures; introduction to survival analysis; multivariate methods.
Prerequisites: BST 621 [Min Grade: C]

BST 623. General Linear Models. 3 Hours.

Simple and multiple regression using matrix approach; weighted and non-linear regression; variable selection methods; modeling techniques; regression diagnostics and model validation; systems of linear equations; factorial designs; blocking; an introduction to repeated measures designs; Coding schemes.
Prerequisites: BST 622 [Min Grade: C]

BST 624. Experimental Design. 3 Hours.

Intermediate experimental design and analysis of variance models using matrix approach. Factorial and nested (hierarchical) designs; blocking; repeated measures designs; Latin squares; incomplete block designs; fractional factorials; confounding. Students should have had matrix algebra as a prerequisite.
Prerequisites: BST 623 [Min Grade: C]

BST 625. Design/Conduct Clinical Trials. 3 Hours.

Concepts of clinical trials; purpose, design, implementation and evaluation. Examples and controversies presented.
Prerequisites: BST 611 [Min Grade: C] and BST 612 [Min Grade: C] or BST 621 [Min Grade: C] and BST 622 [Min Grade: C]

BST 626. Data Management and Reporting with SAS. 3 Hours.

A hands-on exposure to data management and report generation with one of the most popular statistical software packages. Concurrent registrartion in BST 626L is required. Note: Non-degree graduate students will be allowed to register if space permits.

BST 626L. Data Management and Reporting with SAS Laboratory. 0 Hours.

A hands-on exposure to data management and report generation with one of the most popular statistical software packages.

BST 626Q. Data Management and Reporting with SAS. 3 Hours.

This course is designed to provide an introduction to data management and reporting using the SAS system. Students who have some PC computer experience or who have been introduced to SAS are eligible to take this course. Any student taking this course should be familiar with simple summary statistics such as the mean, standard deviation, standard error, median and percentiles as well as proportions. Outside of familiarity with these basic statistics, no other statistical background is required. Though not required, some programming background will be useful as this assures the instructor that the student is familiar with the logic critical in understanding conditional execution commonly used in SAS.

BST 631. Statistical Theory I. 4 Hours.

Fundamentals of probability; independence; distribution and density functions; random variables; moments and moment generating functions; discrete and continuous distributions; exponential families, marginal and conditional distributions; transformation and change of variables; convergence concepts, sampling distributions. Point and interval estimation; hypothesis and significance testing; sufficiency and completeness; ancillary statistics; maximum likelihood and moment estimators; asymptotic properties of estimators and tests; introduction to Bayesian inference. Prerequisite: Advanced Calculus.

BST 632. Statistical Theory II. 4 Hours.

Fundamentals of probability; independence; distribution and density functions; random variables; moments and moment generating functions; discrete and continuous distributions; exponential families, marginal and conditional distributions; transformation and change of variables; convergence concepts, sampling distributions. Point interval estimation; hypothesis and significance testing; sufficiency and completeness; ancillary statistics; maximum likelihood and moment estimators; asymptotic properties of estimators and tests; introduction to Bayesian inference.
Prerequisites: BST 631 [Min Grade: C]

BST 640. Nonparametric Methods. 3 Hours.

Properties of statistical tests; order statistics and theory of extremes; median tests; goodness of fit; tests based on ranks; location and scale parameter estimation; confidence intervals; association analysis; power and efficiency.
Prerequisites: BST 621 [Min Grade: C] and BST 631 [Min Grade: C]

BST 655. Categorical Data Analysis. 3 Hours.

Logistic regression models; regression diagnostics; proportional odds; ordinal and polytomous logistic regression; analyses for multi-way tables; Mantel-Haenszel test; measures of association and of agreement; loglinear and logit models; ordinal discrete data; matched pairs; repeated categorical data. BST 612 or equivalent recommended as a prerequisite.
Prerequisites: BST 622 [Min Grade: C]

BST 660. Applied Multivariate Analysis. 3 Hours.

Analysis and interpretation of multivariate general linear models including multivariate regression, multivariate analysis of variance/covariance, discriminant analysis, multivariate analysis of repeated measures, canonical correlation, and longitudinal data analysis for general and generalized linear models. Extensive use of SAS, SPSS, and other statistical software.
Prerequisites: BST 623 [Min Grade: C]

BST 661. Structural Equation Modelling. 3 Hours.

Basic principles of measurements; factor analysis and latent variable models; multivariate predictive models including mediation mechanisms and moderators effects;path analysis;intergrative mutivariate covariance models, methods of llongitudinal analysis.
Prerequisites: BST 623 [Min Grade: C]

BST 665. Survival Analysis. 3 Hours.

Design and analysis of clinical trials; sample size computation; properties of survival distributions; estimation and hypothesis testing for survival parameters;Kaplan-Meier estimation; exponential tests; Cox proprtional hazards regression models, parametric survival models.
Prerequisites: BST 622 [Min Grade: C]

BST 670. Sampling Methods. 3 Hours.

Simple random, stratified, cluster, ratio regression and systematic sampling; sampling with equal or unequal probabilities of selection; optimization; properties of estimators; non-sampling errors; sampling schemes used in population research; methods of implementation and analyses associated with various schemes.
Prerequisites: BST 631 [Min Grade: C]

BST 671. Meta-Analysis. 3 Hours.

Statistical methods and inference through meta analysis.
Prerequisites: BST 623 [Min Grade: C] and BST 632 [Min Grade: C]

BST 675. Introduction to Statistical Genetics. 3 Hours.

This class wil introduce students to population genetics, genetic epidemiology, microarray and proteomics analysis, Mendelian laws, inheritance, heritability, test cross linkage analysis, QTL analysis, human linkage and human association methods for discrete and quatitative traits.
Prerequisites: BST 611 [Min Grade: C] or BST 621 [Min Grade: C]

BST 676. Genomic Data Analysis. 3 Hours.

Algorithms and methods that underlie the analysis of high dimensional biological data, as well as issues in the design and implementation of such studies. High dimensional biology includes microarrays, proteomics, genomic, protein structure, biochemical system theory and phylogenetic methods. NOTE: Some knowledge of statistics (MTH 180 or BST 621) also some bio-informatics/high dimensional biology training (CS 640, MIC 753, or BST 675 is required. Interested students are urged to contact the instructors with concerns regarding assumed knowledge.
Prerequisites: BST 611 [Min Grade: C] or BST 621 [Min Grade: C]

BST 680. Statistical Computing with R. 2 Hours.

This course is mainly focused on R and how to use R to conduct basic statistical computing. The course contains three themes: R programming, introduction to high performance computing, and basics of statistical computing.
Prerequisites: BST 621 [Min Grade: C] and BST 622 [Min Grade: C] and BST 626 [Min Grade: C] and BST 631 [Min Grade: C] and BST 632 [Min Grade: C]

BST 690. Biostatistical Consulting and Applied Problems. 3 Hours.

Students will work individually to address, analyze and present the results of an applied problem or grant design each week. The presentation of approaches, solutions and designs will be conducted in a round table format. Students will be evaluated on the quality of solution and by their presentation and class participation.
Prerequisites: BST 621 [Min Grade: C] and BST 622 [Min Grade: C]

BST 691. Pre-Doctoral Seminar Series. 1 Hour.

Biostatistics Seminar Series. This course is restricted to Biostatistics in Public Health majors only.

BST 695. Special Topics. 1-3 Hour.

Special topics in Biostatistics not covered in regular 600 level courses, but suited for Masters students in Biostatistics and doctoral students in other related disciplines.
Prerequisites: BST 671 [Min Grade: C](Can be taken Concurrently)

BST 697. Internship in Biostatistics. 3 Hours.

Field Experience under joint direction of appropriate public health faculty member and qualified specialists working in selected aspects of public health.
Prerequisites: BST 601 [Min Grade: C] or (BST 611 [Min Grade: C] and BST 612 [Min Grade: C]) and ENH 600 [Min Grade: C] and EPI 600 [Min Grade: C] and HB 600 [Min Grade: C] and HCO 600 [Min Grade: C]

BST 698. Non Thesis Research. 1-12 Hour.

Independent non-thesis research with guidance of appropriate faculty. Restricted to Biostatistics Majors only or permission of instructor / department.

BST 699. Thesis Research. 1-12 Hour.

Thesis Research under the direction of research committee. At least 6 graduate credits needed for graduation. Must be admitted to candidacy.
Prerequisites: GAC M

BST 723. Theory of Linear Models. 3 Hours.

Multivariate normal distributions and quadratic forms; least square estimation; nested models; weighted least squares, testing contrasts; multiple comparison; polynomial regression; maximum likelihood theory of log linear models will be studied.
Prerequisites: BST 632 [Min Grade: C]

BST 725. Advances Clinical Trails. 3 Hours.

This course will provide students with the tools to develp a basic understanding of the fundamental statistical principles involved in the design and conduct of clinical trials.
Prerequisites: BST 611 [Min Grade: C] and BST 612 [Min Grade: C] or BST 621 [Min Grade: C] and BST 622 [Min Grade: C] and BST 625 [Min Grade: C]

BST 726. Adv Clin Trials II. 3 Hours.

Students will develop a more thorough understanding of the basic methodology behind important statistical concepts used in the design and anaylsis of large randomized clinical trials.
Prerequisites: BST 621 [Min Grade: C] and BST 622 [Min Grade: C] and BST 625 [Min Grade: C] and BST 631 [Min Grade: C] and BST 632 [Min Grade: C] and BST 725 [Min Grade: C]

BST 735. Advanced Inference. 4 Hours.

Families of models; likelihood; sufficiency; significance tests; similar regions; point and interval estimation; invariant tests; asymptotic theory and large sample inference; LR, score and Wald tests; robust procedures will be studied.
Prerequisites: BST 632 [Min Grade: C] and BST 631 [Min Grade: C]

BST 740. Bayesian Analysis. 3 Hours.

To introduce the student to the basic principles and tools of Bayesian Statistics and most importantly to Bayesian data analysis techniques. A major goal is to establish a firm foundation in the discipline upon which the applications of statistical and epidemiologic inference will be built.
Prerequisites: BST 632 [Min Grade: C]

BST 741. Advanced Bayesian Analysis II. 3 Hours.

This course is intended to illustrate advanced Bayesian modeling and computation for variety of models and problems.
Prerequisites: BST 622 [Min Grade: C] and BST 632 [Min Grade: C]

BST 750. Stochastic Modeling. 3 Hours.

Poisson processes; random walks; simple diffusion and branching processes; recurrent events; Markov chains in discrete and continuous time; birth and death process; queuing systems; applications to survival and other biomedical models will be studied.
Prerequisites: BST 632 [Min Grade: C]

BST 760. Generalized Linear and Mixed Models. 3 Hours.

Generalized linear models; mixed models; and generalized estimating equations.
Prerequisites: BST 723 [Min Grade: C]

BST 765. Advanced Computational Methods. 3 Hours.

Numerical algorithms useful in biostatistics including likelihood maximization using the Newton-Raphson method, EM algorithm, numerical integration using quadratic and Monte-Carlo methods, interpolation using splines, random variate generation methods, data augmentation algorithm, and MCMC and Metropolis-Hastings algorithm; randomization tests; resampling plans including bootstrap and jackknife will be studied.
Prerequisites: BST 632 [Min Grade: C]

BST 775. Statistical Methods for Genetic Analysis I. 3 Hours.

This course will provide a staistical basis for describing variation in qualitative (disease) and quantitative traits. This will include decomposition of trait variation into components representing genes, environment and gene-environment interaction. Resemblance between relative and heritability will be described. Important topics of discussion will include oligogenic and polygenic traits, complex segregations analysis, methods of mapping and characterizing simple and complex trait loci. NOTE: It is assumed that students are comfortable with regression theory, covariance, correlation, and likelihood theory. Interested students are urged to contact the instructors with concerns regarding assumed knowledge.
Prerequisites: BST 623 [Min Grade: C] and BST 632 [Min Grade: C] and BST 675 [Min Grade: C]

BST 776. Statistical Methods for Genetic Anlaysis II. 3 Hours.

This course builds on the knowledge gained in BST 775 with rigorous mathematical & statistical treatment of methods for localizing genes and environmental effects involved in the etiology of complex trits using case-control and pedigree data. NOTE: Knowledge of SAS and programming languages such as C++, and basic knoledge of multivariate methods and Markov chain theory is highly recommended.
Prerequisites: BST 775 [Min Grade: C]

BST 793. Post-doc Seminar Series. 3 Hours.

BST seminar series. Permission of instructor / department required.

BST 795. Advanced Special Topics. 1-3 Hour.

This course is designed to cover advanced special topics in Biostatistics that are not covered in regular 700 level courses, but suited for doctoral students in Biostatistics.
Prerequisites: BST 622 [Min Grade: C] and BST 632 [Min Grade: C]

BST 798. Non-Dissertation Research. 1-12 Hour.

Non-dissertation research with the guidance of appropriate faculty. Research conducted before admission to candidacy for the doctoral degree. Biostatistics majors only or permission of instructor / department required.

BST 799. Dissertation Research. 1-12 Hour.

Doctoral Level Dissertation Research under the direction of the dissertation research committee. Reserved for Biostatistics only or permission of instructor /department. Admission to Candidacy required.
Prerequisites: GAC Z

Faculty

Aban, Inmaculada (Chichi), Professor, 2004, Ph.D. (Bowling Green State) , Design, Implementation and Analysis of Clinical Trials, Analysis of Imaging Data, Models for Count Data, Model Diagnostics, Survival and Reliability Analysis, Inference for Heavy Tailed Distributions
Austin, Erika, Assistant Professor, 2015, Ph.D. (Virginia), Health disparities among stigmatized populations, barriers to health care access, LGBT health and well-being
Bartolucci, Alfred A., Professor Emeritus, 1977, Ph.D. (SUNY, Buffalo), Clinical Trials, Survival Analysis, Bayesian Statistics, Longitudinal Data Analysis, Meta-Analysis
Beasly, Timothy Mark, Professor, 2001, Ph.D. (Southern Illinois - Carbondale), Linear Models, Linkage and Association with Quantitative Traits, Nonparametric Methods, Microarray Analysis
Cofield, Stacy S., Associate Professor , 2003, Ph.D. (Virginia Commonwealth), Mixed-Effects Models, Clinical Trial Design, Management, and Analysis, Out-of-Hospital Cardiac Arrest and Resuscitation.
Cui, Xiangqin, Associate Professor, 2004, Ph.D. (Iowa State), Experimental design and data analysis for transcriptome, proteome, metabolome, and microbiome
Cutter, Gary, Professor, 2003, Ph.D. (Texas Health Science Center - Houston), Clinical Trials and Community Studies Trial Analyses, Chronic Disease Epidemiology, Large Scale Data Bases, Multiple Sclerosis, Myasthenia Gravis and Nenatal Trials, Behavioral Studies, Rare Diseases, Hypertension
Howard, George, Professor , 1999, Dr.PH. (North Carolina), Design and Analysis of Multi-center Clinical Trials, Application of Statistical Methods in Epidemiological Studies, Linear Models.
Judd, Suzanne E., Assistant Dean for Undergraduate Education, Associate Professor, 2008, Ph.D. (Emory), Vitamin D, Longitudinal Cohort Studies, Data Management, Stroke, Dietary Patterns and Population Nutrition
Katholi, Charles R., Professor Emeritus, 1970, Ph.D. (Adelphi), Computationally Intensive Statistical Methods, Large Sample Theory, Use of Asymptotic Tests in the Presence of Small Samples, Estimation and Testing Infection Potential by Pool Screening.
Liu, Nianjun, Associate Professor, 2005, Ph.D. (Yale), Statistical Genetics/Genomics and Bioinformatics including genetic linkage and association analysis, haplotype analysis, population genetics, genome-wide-association studies and next-generation sequencing data analysis, and bioinformatics; High-Dimensional Data Analysis including integrative analysis of omics data, imaging data analysis, and other large scale data analysis such as microbiome and miRNA data analysis; Pharmacogenomics, Risk Prediction, and Personalized Medicine; and Other Research including longitudinal data analysis, survival analysis, missing data problems and imputation methodology, prediction, machine learning methods, big data, cluster and classification, and clinical trial
Morgan, Charity, Assistant Professor , 2012, Ph.D. (Harvard), Finite Mixture Models. Bayesian Data Analysis. Multiple Sclerosis. Psychopathology.
Perumean-Chaney, Suzanne, Assistant Professor of Justice Sciences, 2004, B.S., M.S. (Nevada), Ph.D. (SUNY Albany), Quantitative Methods, Violence, Program Evaluation
Redden, David T., Professor and Chair of Biostatistics, 2001, Ph.D. (Alabama), Regression of Diagnostics, Admixture, Association Studies, Group Randomized Trials
Szychowski, Jeffery, Associate Professor , 2007, Ph.D. (Alabama), Clinical Trials, Maternal and Fetal Medicine Studies, Regression Analysis and Smoothing Methods, Categorical Data Analysis, Survival Analysis, Health Administration Research
Tiwari, Hemant, Professor, 2002, Ph.D. (Notre Dame), Genetic Linkage Analysis, Disequilibrium Mapping, Population Genetics, Molecular Evolution, Bioinformatics, and Genetics of Infectious Diseases.
Yi, Nengjun, Professor, 2002, Ph.D. (Nanjing Forestry University, China), Statistical Genetics: genetic association studies of common and rare variants in human populations, statistical analysis of genetic interactions (gene-gene and gene-environment interactions), predictive and prognostic modeling of complex diseases and traits, QTL mapping for complex traits in animal and plant experimental crosses, Statistical Methods for Precision Medicine and Pharmacogenetics, Hierarchical Generalized Linear and Survival Models, High-dimensional Statistical Methods, and Computer Software Development
Zhand, Xiao, Ph.D. (UCLA), Research Assistant Professor. Bayesian Computation, Clinical Trials.
Zhi, Degui, Associate Professor, 2009, Ph.D. (UCSD), Bioinformatics, Statistical Genetics, Genetic and Epigenetics of complex diseases