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Biostatistics (Ph.D., M.S., M.S.P.H.)

View PDF of Biostatistics Admissions Checklist
Prospective students should use this checklist to obtain specific admissions requirements on how to apply to Graduate School.

View PDF version of the Biostatistics catalog description

Biostatistics (Ph.D., M.S., M.S.P.H.)

Degree Offered:

M.P.H., M.S., M.S.P.H., Ph.D.

Director:

Aban

Phone:

(205) 934-2732

E-mail:

This email address is being protected from spambots. You need JavaScript enabled to view it.

Web site:

http://www.soph.uab.edu/bst

Faculty

Inmaculada (Chichi) Aban, Ph.D. (Bowling Green State), Associate Professor. Clinical Trials, Model Diagnostics, Survival and Reliability Analysis, Inference for Heavy Tailed Distributions.

Alfred A. Bartolucci, Ph.D. (SUNY, Buffalo), Professor Emeritus. Clinical Trials, Survival Analysis, Bayesian Statistics, Longitudinal Data Analysis.

Timothy Mark Beasley, Ph.D. (Southern Illinois - Carbondale), Associate Professor. Linear Models, Linkage and Association with Quantitative Traits, Nonparametric Methods, Microarray Analysis.

Stacey S. Cofield, Ph.D. (Virginia Commonwealth), Associate Professor. Mixed-Effects Models, Clinical Trial Design, Management, and Analysis, Out-of-Hospital Cardiac Arrest and Resuscitation.

Xiangqin Cui, Ph.D. (Iowa State), Associate Professor. Microarray Analysis, Quantitative Trait Locus Analysis.

Gary Cutter, Ph.D. (Texas Health Science Center - Houston), Professor and Head of the Section on Research Methods and Clinical Trials. Clinical Trials and Community Studies Trial Analyses, Chronic Disease Epidemiology, Large Scale Data Bases, Multiple Sclerosis, Myasthenia Gravis and Neonatal Trials, Behavioral Studies.

Gustavo de los Campos, Ph.D.  (University of Wisconsin-Madison). Assistant Professor, Section on Statistical Genetics. Quantitative Genetics, Statistical Learning and Prediction, Semi-parametric and Bayesian Methods.

Naomi Fineberg, Ph.D. (Boston University), Research Professor and Chair. Small Medical Studies.

George Howard, DrPH (North Carolina), Professor. Design and Analysis of Multi-center Clinical Trials, Application of Statistical Methods in Epidemiological Studies, Linear Models.

Suzanne E. Judd, Ph.D. (Emory), Assistant Professor. Vitamin D, Longitudinal Cohort Studies, Cystic Fibrosis and Bone Health, Data Management.

Charles R. Katholi, Ph.D. (Adelphi), Professor Emeritus. 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.

Richard Kennedy, Ph.D. (Virginia Commonwealth University), Assistant Professor.  Longitudinal data modeling, cognitive function, clinical trials simulations, gene expression analysis.

Nianjun Liu, Ph.D. (Yale), Associate Professor. Genetic Linkage and Association Analysis, Disequilibrium Mapping, Population Genetics, Bioinformatics, Machine Learning Methods and Longitudinal Data Analysis and Their Applications in Genetics and Bioinformatics.

Xiang-Yang Lou, Ph.D. (Zhejiang), Associate  Professor. Linkage and Association Analysis, Disequilibrium Mapping, Population Genetics, Bioinformatics, Machine Learning Methods and Longitudinal Data Analysis and Their Applications in Genetics and Bioinformatics.

Leslie Ain McClure, Ph.D. (Michigan), Associate Professor and Director of Graduate Studies.  Clinical Trials with Multiple Outcomes, Interim Analysis.

Morgan, Charity Morgan, Ph.D. (Harvard University), Assistant Professor. Finite Mixture Models. Bayesian Data Analysis. Multiple Sclerosis. Psychopathology.

David T. Redden, Ph.D. (Alabama), Professor. Regression Diagnostics, Admixture, Association Studies.

Jeffery Szychowski, Ph.D. (Alabama), Assistant Professor. Clinical Trials, Maternal and Fetal Medicine Studies, Regression Analysis and Smoothing Methods, Categorical Data Analysis, Survival Analysis.

Hemant K. Tiwari, Ph.D. (Notre Dame), Professor and Head of the Section on Statistical Genetics. Genetic Linkage and Association Analysis, Haplotype Analysis, Disequilibrium Mapping, Population Genetics, Molecular Evolution, Bioinformatics.

Laura Kelly Vaughan, Ph.D. (Texas A&M), Research Assistant Professor. Genetic Linkage and Association Studies, Population Stratification, Bioinformatics.

Nengjun Yi, Ph.D. (Zhejiang), Professor. Statistical Genetics/Genomics, Bayesian Statistics, MCMC Algorithms.

Kui Zhang, Ph.D. (Peking), Associate Professor. Statistical Methods for Molecular Biology and Genetics, Linkage and Disequilibrium Analysis, Functional Genomics.

Xiao Zhang, Ph.D. (UCLA), Research Assistant Professor. Bayesian Computation, Clinical Trials.

Degui Zhi, Ph.D. (UCSD), Assistant Professor. Protein sequence and structure analysis, Bioinformatics, Next-generation sequencing data analysis.

General Information

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. Naomi Fineberg is the Chair of the department, Dr. Leslie McClure is the Director of Graduate Studies, and Della Daniel is the department liaison to the graduate program.  The department currently has 24 faculty members, 56 full-time staff, and is organized into two sections: 1) The Section on Statistical Genetics (SSG), led by Dr. Hemant Tiwari and 2) The Section on Research Methods and Clinical Trials (RMCT), led by Dr. Gary Cutter.  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.

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.

The MPH Program

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 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.

Required Courses: MPH in Biostatistics

 MPH Core:

ENH 600

Fundamentals of Environmental Health Sciences 

3 Credit hours 

 

EPI 600
    OR

Introduction to Epidemiology

3 Credit hours

 

EPI 610

Principles of Epidemiology Research

4 Credit hours

 

HB 600

Social and Behavioral Sciences Core

3 Credit hours

 

HCO 600

Introduction to Public Health Systems
And Population-Based Health Programs

3 Credit hours

 

PUH 695

Public Health Integrative Experience

1 Credit hours

 

GRD 727

Writing and Reviewing Research

3 Credit hours

Biostatistics Core:

BST 619

Data Collection and Management

3 Credit hours

 

BST 621

Statistical Methods I

3 Credit hours

 

BST 622

Statistical Methods II

3 Credit hours

 

BST 626/626L

Data Management/Reporting with SAS

3 Credit hours

 

BST 697

Internship in Biostatistics

3 Credit hours

Biostatistics Electives: Minimum 9 credit hours of regular courses of 623 or higher-level.

Outside Electives: A minimum of 3 graduate credit hours of electives must be taken from some field of Biology, Public Health or Medicine. The academic advisor must approve these courses.

The MPH Non Coursework Requirements: The Internship

As a student in the MPH program, you are required to complete three credit hours of an internship experience.  The internship is a field experience which bridges professional academic preparation and public health practice.  Knowledge and skills learned in coursework are applied in an agency setting under the supervision and guidance of an experienced public health specialist.  You may check with the schools internship coordinator Emily Tubergen (This email address is being protected from spambots. You need JavaScript enabled to view it. or 934-7791), or the school’s website at www.soph.uab.edu/internships for internship opportunities.  Faculty research projects are not appropriate venues for an internship, nor are positions which are primarily administrative or focused on data management.

Registering for Internship Experience

Before the hold on the internship course can be lifted, we require that the internship description and agreement form is completed and on file. This form is to be completed in the online internship database Intern Track.  You can log in to this program with your BlazerID and password at www.soph.uab.edu/interntrack.  Your faculty advisor and site supervisor will also be required to sign off on this document, so it is important that you communicate with them as you complete the form, and do not wait until the deadline to register.  A hyperlink allowing you to formally request the hold to be lifted will become active once all the signatures are on file.

You should register under your academic advisor for BST 697 – Internship in Biostatistics.   For three credit hours, you are expected to spend a minimum of 240 hours during the 12 weeks working for the agency.  The internship must be completed in one semester, and all hours must be completed by the last day of exams.  You are required to complete your core course work before registering for internship hours.  Credit cannot be applied retroactively to work you have done prior to registering for the internship.  Students should feel free to contact the Graduate Program Director (Dr. McClure) or Internship Coordinator (Emily Tubergen) if they have any questions or problems during the summer.

Grading and Requirements
The internship is a pass/fail course.  Your grade will be assigned by your faculty advisor based on the completion of all the components below.  All forms related to the MPH internship will be completed in the InternTrack program.

  • Internship Description and Agreement Form
  • Midpoint Meeting Form, and confirmed meetings with the faculty advisor and site supervisor
  • Final student evaluation
  • Final student paper
  • Completion of poster and attendance at the internship poster session
  • Evaluations (Midpoint and Final) from the site supervisor
  • Any additional product required by your internship site

Midpoint meeting: You will be required to complete a midpoint form halfway through your internship.  This is to prompt your reflection on the internship to that point, and steps to make the remainder of the internship a success.  You will set up times to individually meet with your faculty advisor and site supervisor; use the midpoint form as a guide for your conversation.  If you are not able to meet in person, discussions via telephone, email, or Skype will be accepted.  Your faculty advisor and site supervisor will need to confirm the meeting took place in the Intern Track system.

Internship Poster Session: At the end of the internship, prior to the end of exams for that semester, a poster session will be held to showcase the internships completed during that semester.  You will receive additional instructions on creating your poster prior to the event.  Attendance is mandatory, as it is a required component to the internship experience.  Limited exceptions will be made for students completed internships out of the state or country or that are completing the MPH program online.

For complete internship requirements please check out the syllabus on the UAB School of Public Health website: https://www.soph.uab.edu/files/internship/InternshipSyllabus2011.pdf.

The MSPH Program

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 project 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 core set of courses common to all students, plus research elective 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 (CTBS), 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 credit 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.
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.

Coursework
Required Core Courses

 

Credit Hours
14

BST 621

Statistical Methods I

3

BST 622

Statistical Methods II

3

BST 625

Design and Conduct of Clinical Trials

3

EPI 607

Epidemiology of Clinical Research

3

EPI 680

Topics in Clinical Research (P/NP) [1]

2

 

 

 

 

 

 

Masters Research Electives [2]

9

A minimum of 9 credit hours taken from the following courses (selected by faculty advisor and student):

 

BST 619

Data Collection and Management

3

BST 626/626L

Data Management/Reporting with SAS (3 hours)

3

ENH 650

Essentials of Environmental and Occupational Toxicology and Diseases

5

EPI 625

Quantitative Methods in Epidemiology

3

EPI 703

Special Topics in the Epidemiology of Chronic Disease (This course focuses on Writing proposals for funding)

3

EPI 709

Theoretical Basis of Epidemiology

3

HB 624

Advanced Theory and Practice in Behavioral Science

3

HCO 677

Patient-Based Outcomes Measurement

3

 

____________________________________

 

[1] EPI 680 is a two credit hour class in which students attend and participate in lectures provided through the K30 Clinical Studies program at the School of Medicine. The grading is on a Pass/No Pass basis. To earn a grade of Pass, students must attend a minimum of 80% of the lectures over two semesters and participate in all discussion for which they are present.

[2] Care must be exercised when selecting these courses since some have prerequisites that must be taken earlier in the sequence of classes or taken concurrently.

Biostatistics Electives:  Minimum 9 credit hours of regular BST courses of 623 or higher-level. With approval of the advisor, courses included in the research electives that are not taken to meet that requirement may be taken as part of the focus specific electives.

Masters Project Research: Minimum 9 credit hours of supervised research in clinical setting (BST 698).

The MSPH Research Project

The student, with the advice of his/her chosen MSPH project co-directors forms a small committee 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.

The MS Program

The MS degree in Biostatistics is intended primarily for those who wish to acquire a master’s degree with an emphasis in statistical methodology.  Generally, students who anticipate a career performing data management and statistical analysis would enroll in the MS program.  Further, the MS program is the appropriate program to prepare students to enter the PhD.  Successful completion of this degree requires a GPA of 3.0 or better, passing the comprehensive examination at the MS level, completion of a master’s project under the direction of an advisor with committee approval, and oral and written defense of this project.

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.

Required Courses: MS in Biostatistics

Biostatistics Core:

BST 621

Statistical Methods I

3 Credit hours

 

BST 622

Statistical Methods II

3 Credit hours

 

BST 623

General Linear Models

3 Credit hours

 

BST 626

Data Management / SAS

3 Credit hours

 

BST 631

Statistical Theory I

4 Credit hours

 

BST 632

Statistical Theory II

4 Credit hours

 

BST 655

Categorical Data Analysis

3 Credit hours

 

BST 691

Biostatistics Predoctoral Seminar Series

4 Credit hours

Biostatistics Electives: 

Minimum 6 credit hours of regular courses of 624 or higher-level.  For those students planning to go on for the Ph.D., it is a good idea to take more advanced biostatistics courses as electives.

Outside Requirement: 

EPI 610 Principles of Epidemiological Research
(or another comparable course in Epidemiology)

4 Credit Hours

Outside Electives: 

A minimum of 3 additional graduate credit hours of electives must be taken from a non-quantitative field (i.e. Biology, Public Health or Medicine). The academic advisor must approve these courses.

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, 622, 623, 626, 631, 632 and 655. This will be a standard departmental exam, administered by the Graduate Program Committee (GPC). The criteria for evaluation are the candidate’s understanding and competency in basic principles and foundations of biostatistics, 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 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.

The MS 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.

The PhD Program

The PhD degree in biostatistics provides a balance between theory and application.  In addition to providing students with an in-depth understanding of statistical theory and methodology, the main objectives of the program are to train students to become independent researchers, effective statistical consultants and collaborators in scientific research, and effective teachers.
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.

Required Courses: PhD in Biostatistics

All students entering the PhD program are required to complete the coursework required for the MS degree.  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 them to do so, since the completion of the MS project is very good research experience and may lead to a publication.

PhD students are required to take the following courses:

Biostatistics Core:

BST 621

Statistical Methods I

3 Credit hours

 

BST 622

Statistical Methods II

3 Credit hours

 

BST 623

General Linear Models

3 Credit hours

 

BST 626

Data Management / SAS

3 Credit hours

 

BST 631

Statistical Theory I

4 Credit hours

 

BST 632

Statistical Theory II

4 Credit hours

 

BST 655

Categorical Data Analysis

3 Credit hours

 

BST 691

Biostatistics Predoctoral Seminar Series

6 Credit hours

 

BST 723

Theory of Linear Models

3 Credit hours

 

BST 735

Advanced Inference

3 Credit hours

 

BST 760

Generalized Linear and Mixed Models

3 Credit hours

 

BST 765

Advanced Computational Methods

3 Credit hours

Biostatistics Electives: 

 Minimum 12 credit hours of 624 or higher-level regular courses, including at least 9 hours of 700 level courses.

Outside Requirement: 

 EPI 610   

Principles of Epidemiological Research

4 Credit Hours

Outside Electives:   

A minimum of 3 additional graduate credit hours of electives must be taken from a non-quantitative field (i.e. Biology, Public Health or Medicine).
The academic advisor must approve these courses.

 Readings & Research: 

Students are strongly recommended to take Research in Statistics (BST 698) under various faculty members every semester after completion of the first-year equivalent of course work, until a research advisor is chosen.

PhD Qualifying 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, 622, 623, 626, 631, 632 and 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 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 one student needs 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.

PhD Dissertation Research

After forming a graduate committee, the student should present and prepare a written proposal to their committee for suggestions/approval.  The whole committee must approve the proposal, not just the advisor.  This is to ensure that the work is novel, feasible, and significant.  The word “novel” here is important.  A dissertation must add to the body of knowledge in biostatistics, meaning that a careful review of the existing literature on the chosen subject is necessary.  It would be very unfortunate to get to the last stages of your work and to have someone suddenly point out to you that it had already been done!  During the early stages of the research, it may be useful for the student to register for readings courses (BST 798) under the direction of the research advisor.  The purpose of such courses is to review the literature for the research area of interest in order to help the student formulate a research problem.

After a literature survey and a clearer definition of the scope of the proposed research under the direction of the advisor, the student must submit a written proposal and present it orally to the dissertation committee. The dissertation proposal is closed to the general public and should be attended only by the dissertation committee.  The committee may approve unconditionally, approve conditionally, or disapprove the proposal. The oral presentation also represents the oral doctoral candidacy exam.  As such, a student is expected to demonstrate a good understanding of materials relevant to the general field in which the dissertation is written.  The format of the questions for the proposal is left to the discretion of the committee.  The outline and the organization of the proposal must follow the graduate school requirements described in the UAB Graduate Student Handbook. The Dissertation Committee and the Graduate Program Director will recommend the student to the Graduate School Dean for admission to candidacy. The committee meeting at which candidacy is discussed must be scheduled through the Graduate School to allow the Dean to attend. If the proposal is not approved, the student may be given only one other opportunity to re-present the proposal and it must be done within six months of the first attempt.  You must be registered for at least 3 hours in the semester in which you present your project proposal to your committee.

Once the student has (1) passed the qualifying exam at the doctoral level, (2) written a formal dissertation proposal, and (3) had the dissertation proposal approved by the dissertation committee as an acceptable proposal for research, the committee will recommend to the Dean of the Graduate School that the student be admitted to candidacy.  This requires that the student file an “Admission to Candidacy” form with the Graduate School.  A student must be in good academic standing to be admitted to candidacy.  Admission to candidacy must take place at least two semesters before the expected completion of the doctoral program.  Students must be admitted to candidacy before they can register for dissertation research hours (BST 799).   

PhD Final Exam

After the student has completed all formal requirements for the PhD degree, the dissertation committee administers the final oral examination.  The final examination should take the form of a presentation and defense of the dissertation, followed by an examination of the candidate’s comprehensive knowledge of the field.  This examination must be scheduled through the Graduate School to allow attendance of the Dean.  The defense must be announced at least 2 weeks in advance.  It is the responsibility of the student to schedule the defense at a time convenient to all parties involved.  A preliminary copy of the dissertation must be submitted to the dissertation committee for approval at least two weeks prior to the defense, unless otherwise approved in advance by the dissertation committee.  The meeting must be open to all interested parties, publicized on the UAB campus, published in the UAB Reporter, and must take place at least 30 days before the expected date of graduation.  Candidates must be registered for at least 3 semester hours of Dissertation Research (BST 799) during the semester in which the final examination is taken.

The dissertation committee will evaluate the student’s performance in the final exam.  In order for the student to pass, all of the committee or all but one member of the committee must pass the student in the final exam. Upon approval by the committee and the Graduate Program Director, the result of the final exam should be forwarded to the Graduate School Dean for approval. Final copies of the dissertation after final approval of the committee, including any changes required by the committee, must be submitted to the Dean within two weeks following successful completion of the defense. Please see the Graduate Student Handbook for various deadlines and further details. Upon satisfying all requirements, the dissertation committee and the Graduate Program Director will recommend the student to the Graduate School Dean for the doctoral degree.

Admission

Students in the Graduate program are admitted in the Fall semester of each academic year. Applicants for the MS and PhD programs are expected to have a strong foundation in Mathematics. At the very minimum, they should have had a 3-semester sequence of calculus or equivalent and a semester of advanced matrix algebra.  With few exceptions, applicants to the PhD must have a relevant MS degree.  The MPH applicants should also be quantitatively oriented with background in calculus and linear algebra.

Application requirements include completion of the online application form, a non-refundable application fee, official transcripts from all undergraduate coursework and all prior graduate coursework, (International transcripts must be submitted to World Education Services (WES) or Educational Credential Evaluators (ECE) for an official course-by-course credential evaluation; document-by-document evaluations will not suffice), three letters of recommendation (submitted online), a statement of purpose, and Graduate Record Examination (GRE) scores.  International applicants for whom English is not their first language are also required to submit TOEFL scores.  Please note that the department has an ongoing admissions process that begins in February.  Thus, it is recommended that prospective students submit completed applications as early as possible (specifically if financial support is desired).

Minimum admission requirements include: a bachelor’s degree from an accredited college or university, a score of 156 (146 for MPH) on the verbal and 146 on the quantitative sections of the newly revised GRE exam. GRE exams taken prior to August 1, 2011, a minimum score of 1100 on the combined verbal and quantitative sections, with a verbal score of at least 550 (400 for MPH) and a quantitative score of at least 550, a score of 3.5 on the analytic section of the GRE test, and an undergraduate grade point average of 3.0 or better (on a 4.0 scale).  The department also requires a TOEFL score of at least 250 (600 on the old scale) for all international students whose native language is not English. 

Financial Support

Unfortunately, the department is not able to guarantee funding for all students.  However, there are many on-campus part-time employment opportunities with ongoing research projects across campus that are available to qualified students with experience in statistical analysis.  Within reason, the department will work with all students in order to assist them with finding a funding source for their studies.

Fellowships, Traineeships, and/or Assistantships are awarded to well-qualified students.  The financial support of a fellowship or traineeship typically consists of (i) an annual stipend of $22,032 paid over 12 months, and (ii) tuition, fees, and health insurance paid by the department directly to your student account.  The financial support of an assistantship typically consists of (i) an annual stipend of $21,000 paid over 12 months, and (ii) $7,000 additional funds to assist with tuition and etc. (paid as additional salary to the student in two installments in August and January, $3,500 each month).  The financial support is intended to help full-time students in the graduate program.  Accordingly, (i) students must register as a full-time student in approved graduate courses each semester (9 hours fall, 9 hours spring, 3 hours summer) and (ii) students may not engage in any other renumerated activities either on or off campus (exceptions to this rule are rare and require prior approval in writing).  In order to continue receiving financial support students must remain in good standing, continue making satisfactory progress towards their degree, and perform their work in a satisfactory manner.  Should the faculty responsible for the funding source determine that a student fails to meet any of these criteria, he/she forfeits the award.

A research assistantship requires the student to devote approximately 20 hours per week average effort on research/teaching projects under the supervision of a faculty mentor.  Students must be enrolled full-time in order to maintain a research assistantship.  This requires the student to take at least 9 credit hours of coursework during the regular semesters and at least 3 credit hours of coursework during the summer.  Assistantship appointments are typically for one year at a time.

A student fellowship does not require any work effort, but requires the student to register for a greater number of credit hours each semester.  A student on Fellowship is required to take 9 credit hours of coursework during the regular semesters and 6 credit hours during the summer semester.

The department currently has one NIH-funded T32 doctoral training grant. This appointment carries special distinction, is an honor to have, offers certain privileges, and also confer certain obligations.  Trainees are required to pursue their research training on a full-time basis, devoting at least 40 hours per week to the program.  This minimum of 40 hours includes both classroom studies and their research.  All new trainees will receive: 1) “On Being a Scientist” published by the National Academy of Sciences, and 2) a selected list of references on ethical conduct of research.  Trainees are expected to review these materials.  All trainees must complete and maintain IRB training/certification, complete and maintain IACUC training/certification (if they work with animal studies), complete the Principles of Scientific Integrity course (GRD 717), and complete university diversity training.  Trainees are expected to attend all departmental seminars, journal clubs, and grant writing clubs, except when those activities interfere with classes.  Trainees are also expected to attend and ideally present their work at one conference each year.  Funds are available for this.  Additional funds are usually available to allow trainees to attend at least one continuing education workshop outside of UAB each year.  The department holds all T32 students to a very high standard.  The purpose of the T32 training grants is to train future independent scientists, that is, individuals that can direct their own research programs.  Trainees are expected to work actively with a faculty mentor on research projects, with the expectation of co-authoring publications.  All trainees must meet with the T32 program directors twice annually (and additionally as requested) in order to review progress.

Additional Information

Deadline for Entry Term(s):

Fall semester

Deadline for All Application Materials to be in the Graduate School Office:

April 1

Number of Evaluation Forms Required:

At least three

Entrance Tests

GRE (TOEFL is required for international applicants whose native language is not English.)

Contact Information
For detailed information, contact:
Dr. Inmaculada Aban
Director, Graduate Program
1665 University Boulevard, RPHB 414
Birmingham, AL 35294-0022.

Telephone/FAX: (205) 934-2732/ (205) 975-2541

E-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

Web site: www.soph.uab.edu/bst

Course Descriptions

Unless otherwise noted, all courses are of 3 credit hours. Courses in italics are only proposals.

BST 603 – Introductory Biostatistics for Graduate Biomedical Sciences. This course will provide non-biostatistics students seeking a Graduate Biomedical Sciences (GBS) degree with the ability to understand introductory biostatistics concepts. 3 hours.  As needed.

BST 611. Intermediate Statistical Analysis I.- 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). 3 hours. Fall/Spring.

BST 612. Intermediate Statistical Analysis II.-This course will introduce students to the basic principle of tools of simple and multiple regression. A major goal is to establish a firm foundation in the discipline upon which the applications of statistical and epidemiologic inference will be built. Prerequisite: BST 611 or Permission of Instructor. 3 hours. Spring/Summer.

BST 613. Intermediate Statistical Analysis III. Continuation of concepts in BST 611/612, intended to introduce students to additional general concepts in biostatistics beyond an introductory level.  The course will include a broad overview of three areas: 1) categorical, ordinal, and count methods with proportional odds model and Poisson regression; 2) survival analysis and event outcome data with Kaplan-Meier, proportional hazards, and repeated events; 3) repeated measures, mixed models, hierarchical modeling for longitudinal and missing data.  Study design, analysis, interpretation of results, power and sample size estimation, and non-parametric alternatives will be presented for all topic areas. Prerequisite: BST 612. 3 hours. Fall.

BST 619. Data Collection and Management. 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., SAS). Exposure to other software packages as time permits. Prerequisites: BST 611; Previous computer experience or workshop on microcomputers highly recommended. 3 hours. Spring.

BST 620. Applied Matrix Analysis.-Vector and matrix definitions and fundamental concepts; matrix factorization and application. Eigenvalues and eigenvectors, functions of matrices, singular and ill-conditioned problems. Prerequisite: BST 622. 3 hours. As needed.

BST 621. Statistical Methods I. 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 and analysis of variance. Prerequisites: A year of calculus and linear algebra. 3 hours. Fall.

BST 622. Statistical Methods II. Continuation of concepts in BST 621, extended to multiple linear regression; analysis of variance; analysis of covariance; multiple analysis of variance; use of contrasts and multiple comparisons procedures; simple and multiple logistic regression; introduction to survival analysis. Prerequisites: BST 621. 3 hours. Spring.

BST 623. General Linear Models. Simple and multiple regression using matrix approach; weighted and nonlinear 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. Prerequisite: BST 622. 3 hours. Fall.

BST 624. Experimental Design. 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. Prerequisites: Matrix algebra and BST 623. 3 hours. As needed.

BST 625. Design and Conduct of Clinical Trials. Concepts of clinical trials; purpose, design, implementation and evaluation. Examples and controversies presented. Prerequisites: BST 611 and 612 or permission of the instructor. 3 hours. Pass/No Pass. Summer.

BST 626/626L. Data Management/Reporting with SAS. A hands-on exposure to data management and report generation with one of the most popular statistical software packages. Concurrent registration in BST 626 and BST 626L is required. 3 hours. Fall.

BST 631. Statistical Theory I. Fundamentals of probability; conditional probability and independence; distribution, density, and mass functions; random variables; moments and moment generating functions; discrete and continuous distributions; exponential families, joint, marginal, and conditional distributions; transformation and change of variables; convergence concepts; sampling distributions; order statistics; random number generation. Prerequisites: Advanced calculus. 4 hours. Fall.

BST 632. Statistical Theory II. Point interval estimation; sufficiency and completeness; ancillary statistics; maximum likelihood and moment estimators; best unbiased estimator; hypothesis and significance testing; likelihood ratio tests and uniformly most powerful tests; confidence interval estimation; asymptotic properties of estimators and tests; introduction to Bayesian inference. Prerequisite: BST 631. 4 hours. Spring.

BST 640. Nonparametric Methods. 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. Prerequisite: BST 622, BST 632. 3 hours. As needed.

BST 655. Categorical Data Analysis – Intermediate level course with emphasis on understanding the discrete probability distributions and the correct application of methods to analyze data generated by discrete probability distributions. The course covers contingency tables, Mantel-Haenszel test, measures of association and of agreement, logistic regression models, regression diagnostics, proportional odds, ordinal and polytomous logistic regression, Poisson regression, log linear models, analysis of matched pairs, and repeated categorical data. Prerequisite: BST 622 or equivalent recommended. 3 hours. Fall.

BST 660. Applied Multivariate Analysis. Analysis and interpretation of multivariate general linear models including multivariate regression, multivariate analysis of variance/covariance, discriminant analysis, repeated measures, canonical correlation, and longitudinal data analysis for general and generalized linear models. Extensive use of SAS, SPSS, and other statistical software. Prerequisite: BST 623. 3 hours. As needed.

BST 661. Structural Equation Modeling. Basic principles of measurements; factor analysis and latent variable models; multivariate predictive models including mediation mechanisms and moderator effects; path analysis; integrative multivariate covariance models, methods of longitudinal analysis. Prerequisite: BST 623. 3 hours. As needed.

BST 665. Survival Analysis. Kaplan-Meier estimation; Parametric survival models; Cox proportional hazards regression models; Sample size calculation for survival models; Competing risks models; multiple events models. Prerequisite: BST 622. 3 hours.

BST 670. Sampling Methods. 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. Prerequisite: BST 631. 3 hours. As needed.

BST 671. Meta Analysis. Statistical methods and inference through meta analysis. Prerequisite: BST 623, BST 632. 3 hours. As needed.

BST 675. Introduction to Statistical Genetics. This class will 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 quantitative traits. Prerequisite: BST 611 or BST 621. 3 hours. Spring.

BST 676. Statistical Bioinformatics. The purpose of this class will be to teach graduate students statistics methods that underlie the analysis of data generated by high throughput genomic technologies, as well as issues in the experimental design and implementation of these technologies. High throughput technologies that will be covered include microarrays, proteomics, and second generation sequencing. Prerequisites: BST 611 or 621.  BST 675 recommended. 3 hours. Spring (odd years).

BST 691 – Biostatistics Pre-doctoral Seminar Series.  This course provides an opportunity for students to learn about ongoing research in the field of biostatistics, clinical trials, and statistical genetics.  Pass/No Pass.  1 hour.  Fall/Spring.

BST 695. Special Topics. This course is designed to cover special topics in Biostatistics that are not covered in regular 600 level courses, but suited for Masters students in Biostatistics and doctoral students in other related disciplines. 1-3 hours.

BST 697. Internship in Biostatistics. Pass/No Pass. 1-6 hours.

BST 698. Non-thesis Research. Pass/No Pass. 1-6 hours.

BST 699. Master’s Thesis Research. Prerequisite: Admission to candidacy for MS Degree. Pass/No Pass. 1-12 hours.

BST 723. Theory of Linear Models. Multivariate normal distributions and quadratic forms; least square estimation; nested models; weighted least squares; testing contrasts; multiple comparisons; polynomial regression; maximum likelihood theory of log-linear models. Prerequisite: BST 632. 3 hours. Fall (odd years).

BST 725. Advanced Clinical Trials I. This course will provide students with a basic understanding of the fundamental statistical principles involved in the design and conduct of clinical trials.  Important topics of discussion will include data management, quality assurance, endpoints, power analysis, interim analysis, adaptive designs, and genetic issues in clinical trials.  Prerequisites: BST 611, 612 and 625. 3 hours. Fall (even years).

BST 726. Advanced Clinical Trials II. This course builds on the knowledge gained in BST 725 in order to develop a more thorough understanding of the basic methodology behind power analysis, interim data monitoring, analysis of missing data, and adaptive designs.  The class involves discussions of recent publications dealing with current topics of interest in clinical trials.  Each student must conduct, summarize, and present a course project based on a more in-depth exploration of one of the topics introduced in the BST 725 course.  Prerequisites: BST 621, 622, 625, 631, 632 and 725. 3 hours. Spring (odd years).

BST 735. Advanced Inference. Stochastic convergence and fundamental inequalities; weak convergence and the central limit theorems; large sample behavior of the empirical distribution and order statistics; Asymptotic behavior of estimators and tests with particular attention to LR, score and Wald tests. Prerequisites: BST 631 and 632. 3 hours. Spring (odd years).

BST 740. Bayesian Analysis. 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. The practical part of the course will be based on Bugs (either WinBugs or OpenBugs), possibly accessed through R with the existing tools for the interface (R packages: R2WinBUGS or BRugs, coda). This will enable participants to take the practical examples all the way to the reporting stage in terms of tabulations and graphics. Prerequisites: BST 632. 3 hours. Fall (even years).

BST 741. Advanced Bayesian Analysis II.  To illustrate advanced approaches to Bayesian modeling and computation in statistics. We begin with a brief description of the basic principle and concepts of Bayesian statistics. We then study advanced tools in Bayesian modeling and computation.  A variety of models are covered, including multilevel/hierarchical linear and generalized linear models, models for robust inference, mixture models, multivariate models, nonlinear models, missing data, and Bayesian model selection. We also introduce some applied areas of modern Bayesian methods, such as genetics/genomics and clinical trials. The practical part of the course will be based on Bugs (either WinBugs or OpenBugs), possibly accessed through R with the existing tools for the interface (R packages: R2WinBUGS or BRugs, coda). This will enable participants to take the practical examples all the way to the reporting stage in terms of tabulations, graphics etc. Prerequisites: BST 631 and 632. BST740 would be helpful but not absolutely required. Fall (odd years).

BST 750. Stochastic Modeling. 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. Prerequisite: BST 632. 3 hours. As needed.

BST 760. Generalized Linear and Mixed Models. Generalized linear models; mixed models; and generalized estimating equations. Prerequisite: BST 723. 3 hours. Spring (even years).

BST 765. Advanced Computational Methods.-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, MCMC and Metropolis-Hastings algorithm; randomization tests; resampling plans including bootstrap and jackknife. Prerequisites: BST 632. 3 hours. Fall (even years).

BST 775. Statistical Methods for Genetic Analysis I. - This course will provide a statistical 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 relatives 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. Prerequisites: BST 623, BST 632, and BST 675. 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. 3 hours. Fall (odd years).

BST 776. Statistical Methods for Genetic Analysis II.- This course builds on the knowledge gained in BST 775 with rigorous mathematical and statistical treatment of methods for localizing genes and environmental effects involved in the etiology of complex traits using case-control and pedigree data. Prerequisites: BST 775; Knowledge of SAS and programming languages such as C++, and basic knowledge of multivariate methods and Markov chain theory is highly recommended. 3 hours. Spring (even years).

BST 793 – Biostatistics Post-doctoral Seminar Series.  This course provides an opportunity for post-doctoral students to learn about ongoing research in the field of biostatistics, clinical trials, and statistical genetics. Pass/No Pass.  3 hours. Fall/Spring.

BST 795. Advanced Special Topics. This course is designed to cover advanced special topics in Biostatistics not covered in regular 700 level courses, but suited for doctoral students in Biostatistics. Prerequisites: BST 622 & 632. 1-3 hours.

BST 798. Non-dissertation Research. Pass/No Pass. 1-6 hours.

BST 799. Doctoral Dissertation Research. Prerequisite: Admission to candidacy for PhD. Pass/No Pass. 1-12 hours.