Biostatistics Student Handbook


The overall mission of the Department of Biostatistics has three components. The first is to provide excellent education in biostatistical theory and methods for students in the Department of Biostatistics, the College of Public Health, the Carver College of Medicine, and the University of Iowa. The second component is to conduct outstanding biostatistical research and to collaborate with investigators in conducting outstanding health science research. The third is to use our skills to serve the College of Public Health, the Carver College of Medicine, The University of Iowa, the State of Iowa, and the wider health science community.

Educational Mission

The teaching mission of the Department of Biostatistics is to provide an excellent education in the theory and application of statistical methods used in the health sciences. The scope of this mission covers courses tailored to the other departments within our college, especially epidemiology; introductory courses intended for other colleges, and courses for the training of biostatisticians at the M.P.H., M.S., and Ph.D. levels. For the training of biostatisticians, this program will prepare students to excel in a variety of occupations, including academic positions in colleges of medicine and schools of public health; positions in pharmaceutical and other health-related industries; and positions in local, state, and federal governmental health agencies. All students will be trained in the skilled use of a variety of relevant biostatistical procedures and their software implementation and will gain practical experience by working on collaborative medical projects. Furthermore, Ph.D. students will learn the fundamentals of statistical and biostatistical theory, enabling them to read the biostatistical research literature and to contribute to it.

Administrative Organization

The Department of Biostatistics is one of five departments in the College of Public Health: Biostatistics, Community and Behavioral Health, Epidemiology, Health Management and Policy, and Occupational and Environmental Health.

The Head of the Department of Biostatistics is Dr. Joseph Cavanaugh, who is responsible for administration of the educational, research, and professional service functions of the Department. Dr. Jacob Oleson serves as Director of Graduate Studies. The department currently has 13 primary faculty. Ms. Terry Kirk is Graduate Program Administrator.

More information on the Department of Biostatistics and the College of Public Health can be found at the College of Public Health website

Student Organizations, Committees, and Service Opportunities

The Biostatistics Student Organization (BSO) is officially chartered with The University of Iowa Student Organization Business Office. Any Biostatistics student is automatically a member. Meetings and activities are arranged by students. Officers are elected annually to lead the organization and act as a liaison with departments and colleges.

The Biostatistics Student Organization also conducts elections to select a departmental representative to the University of Iowa Graduate Student Senate (GSS). Students are occasionally called upon to interact with prospective faculty and students and other visitors, and to provide other services to the department, to the college, and to fellow students.

Each fall the Dean of the College of Public Health invites selected students to participate as members on standing and ad-hoc collegiate committees. These students act as sources of information for the student body and offer an opportunity for student concerns and opinions to be aired. More information on collegiate committee opportunities is available in the College of Public Health Dean’s office.

The American Statistical Association (ASA) offers student memberships at reduced rates to full-time students. Application information is available at the membership portion of the ASA website. The International Biometric Society (Eastern North American Region or “ENAR”) also offers discounted memberships to students (see

The College of Public Health Student Association (CPHSA) at The University of Iowa was established to advocate for opportunities in professional development and outreach, discuss student issues, and create a greater sense of community for all students in the College of Public Health. For more information about this organization, see the College’s CPHSA web page.


Minimum Requirements for Admission

The minimum grade-point-average requirement is 3.0 for admission to either the M.S. program or the Ph.D. program. The Graduate Record Exam (GRE) is required. The recommended minimum score on the combined verbal and quantitative portions is 305; however, the average combined score for new students in recent years has been between 315-330.

Non-U.S. citizens are required to take the Test of English as a Foreign Language (TOEFL) or the International English Language Testing System (IELTS), unless they have a degree from an accredited college or university in the U.S., the UK, Canada (except Quebec), Australia, or New Zealand.

A TOEFL minimum score of 250 (computer-based), 600 (paper-based) or 100 (internet-based) is required.

The IELTS as certification of English language proficiency will be accepted. The minimum IELTS score is 7.0 to be considered for admission (with no subscore lower than 6.0). All students submitting scores from the IELTS must take the on-campus English Proficiency Evaluation (EPE) prior to their first UI registration. Remedial English courses may be required based on this evaluation.

Required prerequisites for admission are training in single-variable and multi-variable differential and integral calculus and vector algebra, as well as ability to program in at least one computer language. In addition, completion of a master’s in statistics or biostatistics is generally required for admission to the Ph.D. program.

Previous coursework or experience in statistical methods or data analysis is preferred.

Currently enrolled University of Iowa (UI) graduate students seeking a Master’s degree in Biostatistics concurrently with their primary major must complete a minimum of 15 of the 38 s.h. degree requirements after acceptance to the program. Approval of the home department advisor is required. Applications from current UI graduate students should be received by January 15th for consideration of admission the subsequent fall. Please contact the Graduate Program Administrator for the application requirements.

Policy for “Internal” Application to Ph.D. Program

Students in the University of Iowa’s M.S. program in Biostatistics who have a desire to remain in the department to pursue a Ph.D. need to formally apply for the PhD program by December 1st of their second year, by submitting to the Graduate Program Administrator the following:

  1. An updated résumé
  2. A new “Statement of Purpose”
  3. Two letters of recommendation. One reference should be an individual who can comment on the applicant’s potential as a collaborative researcher and/or a teacher.

The Ph.D. Admissions Committee will consider the above items along with other information already contained in the applicant’s file (M.S. Core Exam scores, GRE scores, transcript, previous application materials, etc.). Students should be aware that excellent academic performance does not guarantee acceptance into the Ph.D. program. Many other factors are considered, such as communication skills, technical skills, ability to perform as a Research Assistant and/or a Teaching Assistant, demonstrated ability to take initiative, leadership skills, and the willingness and availability of faculty members to serve as the dissertation advisor. The Ph.D. Admissions Committee will attempt to make a decision on the application by mid February. Those who are offered admission for the Fall Semester will have until April 15 to accept or decline the offer.

Exceptional candidates who wish to transfer early into the Ph.D. program may, with the approval of their advisor, apply during the fall of their second year to be admitted effective in the subsequent Spring Semester. For these early applications, October 15 is the deadline for submitting the materials listed above, and the M.S. Core Exam must be passed prior to applying. The Biostatistics faculty will attempt to render one of the following decisions by the end of November: acceptance, denial, or deferral. Deferred applications will be reconsidered with others who make the usual December 1st deadline, for consideration for regular Fall admission. Students whose applications are denied or deferred are encouraged to consult with their advisor.


If a student’s enrollment is interrupted for any reason so that s/he is not enrolled for three consecutive academic sessions (including the spring, summer, and fall sessions but excluding the winter session) the student must apply for readmission. A readmission application form must be submitted. The Graduate College will not require new letters of recommendation, a new Personal Statement section, a written explanation of the reasons for the absence, nor a plan for degree completion.

Financial Support

Deadlines for Applicants

The Biostatistics Department application deadline for admission and consideration of financial aid is December 1 for fall of the following year. The availability of financial aid is less likely for individuals who miss this deadline. Students who are accepted into the program may be offered a teaching assistant or research assistant position; some students are offered admittance without financial support.

Graduate Assistantships

Research assistantships are available for work in the Biostatistics Consulting Center or on specific research projects. Teaching assistantships are also available. Competitive fellowships may be available through the Department, The University of Iowa, pharmaceutical firms, and the National Institutes of Health.

Most Biostatistics students receive financial aid by working 10-20 hours per week as research assistants or teaching assistants. Working a minimum of 10 hours per week (a 1/4-time position) each semester reduces the graduate college tuition to the in-state level and provides a stipend, a tuition scholarship, and contributions toward health insurance (see Graduate Assistant Employment).

Policies Concerning Financial Support

Offers of financial aid as graduate assistants are subject to satisfactory performance of duties, adequate academic performance in our program (GPA≥3.30), and full-time registration for at least 9 semester hours during both semesters. Should a student’s GPA be less than 3.30 after one semester, his/her advisor, together with the Director of Graduate Studies, will determine whether it is in the student’s best interests to continue with the assistantship, or whether the financial support should be decreased or eliminated to allow the student to focus on coursework. Similarly, a graduate assistant may lose financial aid if his/her job performance is unsatisfactory. Examples of unsatisfactory performance include, but are not limited to: unreliability in completing assignments; missing office hours, classes, lab sections, or required meetings; disrespectful treatment of others; etc.

Toward the end of each academic year, the Director of Graduate Studies and other departmental faculty will review the performance and progress of all graduate students to determine which assistantships should be renewed for the subsequent year. Financial aid will generally continue if a cumulative GPA of 3.50 is maintained, duties are performed in a satisfactory manner, and the student continues to be registered full-time each Fall and Spring semester.

New opportunities for assistantship positions sometimes arise during the year. In such cases, the Director of Graduate Studies will review the progress and status of all students and determine which student(s) to refer for an interview for such positions. Some of these opportunities are appropriate for students who do not yet hold an assistantship. Other opportunities require that a more advanced student be asked to switch efforts to the new position, which could potentially provide an opportunity for a less-experienced student to fill the vacancy created by the switch. All students should keep their résumés current should these opportunities arise.

Some financial aid is available in the summer through research assistantships, and through a limited number of teaching assistantships for the summer session. It is generally easier for research assistants to find summer support than it is for teaching assistants. In fact, some investigators outside of the department have needs for additional biostatistical support in the summer and may decide to increase the percent of time that they support a graduate research assistant. Furthermore, international students often have relatively few options for summer employment other than their assistantships. All of these factors may lead to perceived inequities among the students. In response to this, the department tries to balance financial support as much as possible; however, this is not always possible, as resources are limited and sometimes controlled by investigators outside of the department. Academic performance, previous experience, and aptitude are considered when determining the order of priority for financial support.

These policies only pertain to assistantships that are controlled or facilitated by the Department of Biostatistics. Students may also seek graduate assistantships in their areas of interest in departments outside of Biostatistics.

University of Iowa Policies Affecting Students

Students should review University of Iowa Policies Affecting Students. Topics addressed include the student bill of rights, standards of academic conduct, treatment of student educational records, policies on sexual harassment, disability policy, religious diversity, and grievance procedures. Students who believe there has been a violation can contact the Dean of Students Office to discuss options available for reporting incidents to the appropriate authorities.

Graduate College Regulations

All Biostatistics degrees are conferred through the Graduate College. Therefore, we adhere to all Graduate College rules, regulations, and requirements that are outlined in the Manual of Rules and Regulations of the Graduate College. Students should familiarize themselves with the Graduate College regulations. This site includes valuable information and advice about the Iowa City area and University of Iowa for current and prospective students.

Policy on Student Academic Conduct Standards and Procedures

Standards of Academic Conduct

The faculty of the College of Public Health expects the conduct of a student registered or taking courses in the College to be consistent with that of a professional person. Courtesy, honesty, and respect should be shown by students toward faculty, guest lecturers, administrative support staff, and fellow students. Similarly, a student should expect faculty to treat them fairly, showing respect for their ideas and opinions and striving to help them achieve maximum benefits from their experience in the College. Specific guidelines that address student conduct maybe found in the University of Iowa Operations Manual, Part IV, Students, Chapter 1: General Regulations Applying to Students.

Student academic misconduct includes behavior involving plagiarism, cheating, fabrication, falsification of records or official documents, intentional misuse of equipment or materials, and aiding and abetting the perpetration of such acts. The preparation of reports, papers, and examinations, assigned on an individual basis, must represent each student’s own effort. Reference sources and citations should be indicated clearly and adequate attribution given. The use of assistance from other students or aids of any kind during a written examination, except when the use of books or notes has been approved by an instructor, is a violation of the standard of academic conduct. The program position supports the Graduate College policy which can be found in the Graduate College Manual, Section IV.

Procedure for Handling Alleged Violations of Standards of Academic Conduct

Questions of academic dishonesty arising within the College are treated on an individual basis. In the Graduate College, the questions are handled at the departmental level. If the departmental decision is appealed, the Associate Dean for Education and Student Affairs may appoint an appeals committee of faculty and students from a slate of nominees prepared by the Graduate Council and the Graduate Student Senate to recommend an appropriate course of action. Students in professional graduate colleges should inquire at the office of their respective dean for further information. If the student disagrees with the decision made by the Dean, the student may request a review by the Provost.

Department of Biostatistics Requirements

Department of Biostatistics requirements, which are supplemental to the Graduate College regulations that can be found in the Manual of Rules and Regulations of the Graduate College, include:


When an applicant is admitted to the Department of Biostatistics, the student is assigned a faculty advisor by the Director of Graduate Studies.  If a student wishes to change advisors, the student initiates the change by determining which faculty advisor would be preferred and discussing the possibility with the preferred faculty advisor. Upon approval by the new faculty advisor, the student must then notify the prior advisor, the Director of Graduate Studies, and the Graduate Program Coordinator. It should be emphasized that the reason for change may be personal or because of the student’s interests, and that there is no requirement that a student remain with the same advisor throughout that student’s academic career.


Note: International students are subject to registration requirements in addition to those listed below. They are generally required to be registered full-time (at least 9 s.h.) in fall and spring semester, and there are restrictions on the number of courses they are allowed to register for via distance learning, e.g. web classes. International students should contact the Graduate Program Administrator and/or the Office of International Student and Scholar Services (ISSS) if they have questions about registration requirements in specific situations.

Nine or more semester hours constitutes full-time enrollment during fall and spring semester. An M.S. or Ph.D. student may register for no more than 15 semester hours per semester during fall and spring semester, 8 s.h. during the 8-week summer session, 6 s.h. during the 6-week summer session, or 3 s.h. during the 3-week summer session.

Doctoral students who are post comp may register for less than 9 s.h. and be considered full-time students for the purposes of financial aid. A short-hours registration form will need to be submitted to the Office of the Registrar. Please contact the Graduate Program Administrator to determine eligibility.

Computer registration is done on the University of Iowa MyUI registration system on the University of Iowa website. Registration instructions are available on the university website and at new student orientation. New students must have presented completed and valid health forms to Student Health & Wellness before being allowed to register; this is required of all students in any of the University of Iowa health sciences colleges. New international students must also participate in an orientation conducted by the Office of International Students and Scholars before being allowed to register for their first session at the University of Iowa.

To register, a student must first obtain electronic authorization from their faculty advisor. The student or the advisor should contact the Graduate Program Administrator for assistance with registration as needed.

Changing Registration

MyUI has a link with the Registrar listing significant academic deadlines for each semester, including deadlines for changes or withdrawal of registration and financial penalties involved.

Changes in registration must be initiated by the student. Students may change registration with no penalty via computer until midnight the day prior to the start of classes. During the first five days of the semester, any change should be completed electronically on MyUI; thereafter a paper change in registration form may need to be completed.   Students should be aware that failure to drop classes by the established deadline will result in a successively increased percentage of tuition fee assessment.


Plus/Minus Grading

Plus/minus grading is an option in Department of Biostatistics courses. Students may check with each course instructor at the beginning of the semester to determine if the option will be used.


A grade of Incomplete (“I”) is to be used only when a student’s work during a session cannot be completed because of illness, accident, or other circumstances beyond the student’s control. The student must submit required work with sufficient time for the instructor to review it and submit a grade by the end of the next semester. Failure to do this results in a grade of “F.” Students with “I” from spring semester have until the end of the following fall semester to remove an “I.”

Satisfactory/Unsatisfactory Grading

A grading system of S/U (Satisfactory/Unsatisfactory) rather than letter grading may be used for courses taken outside the major department, provided that the course instructor and the student’s advisor approve the registration. Arrangements for S/U grading in these courses are accomplished by filing a form with appropriate signatures and submit to the Registrar’s Office at the time of registration or no later than the last day of the second week of a semester. Under S/U grading, the student receives credit for the course if the course is completed satisfactorily, but the course is not included in calculating the grade-point average.

In registrations for any thesis research, independent study, or seminar classes, S/U grading may be applied automatically at the discretion of the instructor.

Retaking a Course

For courses that are repeated, the Graduate College does not have a ‘second-grade only’ policy (where re-taking a course results in the replacement of the original  grade). Therefore, re-registering for a course will not result in the removal of the original grade from a transcript. The two ways to remove a grade from a transcript are (1) by a retroactive withdrawal granted from the Graduate College (approved only in rare cases) or (2) by an instructor-initiated grade change.

Departmental Plan of Study

A departmental plan of study must be submitted within the first semester of study. The purpose of the plan is to ensure that any requested course waivers or transfer credits are approved, and that the student will have completed the appropriate coursework to receive the degree. Plans of study for new students will be discussed during a session conducted by the Graduate Studies Director at the department’s orientation.

The departmental Plan of Study should be completed and signed by the student and the student’s advisor, and submitted to the Graduate Program Administrator for review. The student and his/her advisor will then be informed if the Plan is being returned for modification, or if it is approved. Changes in the Plan of Study must be made within five days of the semester of change.

The Plan of Study for M.S. or Ph.D. students is available on the Department of Biostatistics website in Forms under Current Students. M.P.H. students should consult the M.P.H. Student Handbook for the electronic form used in that program.

Waiver of Courses

Students may request that a required course be waived. A waiver means that the student is not required to enroll in the course, and the student does not receive credit for the course. Examples of appropriate use of a waiver include completion of the course more than ten years prior to anticipated graduation or completion of the course as an undergraduate student.

Transfer Credits

Students requesting transfer of credit hours must include information about the course (institution, course title, number of credit hours, and grade) and a course description sufficient to determine whether it is an acceptable substitute for the replaced course. Transfer credits from other colleges and universities are also evaluated by the Graduate Admissions Office. The department cannot approve transfer hours from other institutions unless the Graduate Admissions Office awards graduate credit hours.

Academic Standing

Any Biostatistics student who receives more than six semester hours of C+ or lower on courses included in the student’s plan of study, including any transfer hours, will be dismissed from the program. Any student who does receive more than six semester hours of C+ or lower may appeal the dismissal in writing to the Head of the Department. Student appeals must be voted on by the Department faculty within two semesters, including summer session, from the end of the semester in which the last C+ or lower grade was received.

While pursuing a degree, students are expected to maintain a 3.00 or better grade-point average. A student with less than a 2.75 G.P.A. (for M.S.) or 3.00 G.P.A. (for Ph.D.) after 8 or more semester hours of graduate work will be placed on probation by the Graduate College. Refer to Sec. IV. of the Manual of Rules and Regulations of the Graduate College for details on probation and dismissal standards, procedures, and appeals.

Application for Degree

A student is required to file an Application for Graduate College Degree by the posted deadline of the session (fall, spring, or summer) in which the student intends to graduate. The Degree Application link is on MyUI under Student Information.

The Graduate Program Administrator will file associated required Graduate College documentation for graduation in consultation with the student and the advisor.

M.S. in Biostatistics

Degree Requirements

Preceptorship Guidelines

The Preceptorship in Biostatistics (BIOS:7500) is a mentored research project involving the application of skills and knowledge acquired elsewhere in the curriculum. Preceptorship projects are supervised by Biostatistics faculty (primary, secondary, or adjunct), and may also involve other collaborators in the department, the college, the university, a governmental agency, or private industry. Other rules governing the preceptorship include the following:

  1. The student and the faculty supervisor should meet at the beginning of the preceptorship to discuss the educational and scientific goals of the preceptorship. They should also discuss general expectations, such as the anticipated format and time frame of the components of the project.
  2. Preceptorship projects must be motivated by real-world scientific questions, which may be addressed through data analysis, simulation studies, and/or methodological investigations.
  3. The students are encouraged to demonstrate initiative and creativity in addressing the scientific questions, while incorporating the advice given by their preceptorship supervisor. In collaborative projects, students should demonstrate appropriate teamwork.
  4. Generally, the preceptorship is taken for 3 s.h, and must involve approximately 135 hours of work (similar to lecture-style courses). If the student already has sufficient experience in Biostatistical collaborations (as determined by the student’s advisor and the Director of Graduate Students), a student may choose to take the preceptorship for 1 s.h. (approximately 45 hours of work). The hours spent on the preceptorship must be in addition to any work the student spends on their regular paid assistantship (e.g., work as a research assistant).
  5. Letter grading must be issued.
  6. A written report is a required component of the course. The supervisor will decide how to incorporate this in the overall grading of the course.
  7. An oral presentation is required. The length of the presentation will be 15 minutes, and 5 minutes will be allowed for questions after the presentation.
  8. A feedback form will be given to those who attend the oral presentation (faculty and other students), to be made available to the supervisor and student as part of the evaluation process.

Preceptorship presentations are generally scheduled towards the end of the fall and spring semesters. The scheduling of presentations at alternate times must be approved by the Director of Graduate Studies.

Master’s Core Examination

The Master’s core examination is a written in-class two-day exam focusing on the five required biostatistics and statistics courses. The exam is three hours each day. The first day covers STAT:4100 and STAT:4101 and the second day covers BIOS:5710/5720/5730. Each of the five exam sections is expected to be approximately 60 minutes in length.

This exam is offered twice per year, in late July and mid-January. The exam may be repeated once. M.S. students are expected to take the examination before the start of their third semester. Copies of past exams are available for review on the shared network folder: U:\Shared by All\Biostat\MS Core Exams. Any student who has a disability that may require some modification of seating or testing must inform the Graduate Program Administrator when intent is declared to take the examination.

Outline of Topics Covered on the M.S. Core Examination:

  1. Probability
    1. Definitions and basic rules
    2. Combinations and permutations
    3. Conditional probability and Bayes’ theorem
    4. Probability density functions, probability mass functions, cumulative distribution functions
    5. Joint, conditional, and marginal distributions
    6. Expected values and moments
    7. Moment-generating functions
    8. Discrete distributions—Bernoulli and binomial, hypergeometric, Poisson, multinomial
    9. Continuous distributions—uniform, normal, c2 , t, F, exponential and gamma, beta, Cauchy
    10. Distributions of functions of random variables; order statistics
    11. Chebyshev’s inequality, central limit theorem
  2. Inference
    1. Properties—sufficiency, unbiasedness, completeness, consistency
    2. Point estimation—method of moments, maximum likelihood, least-squares
    3. Cramer-Rao inequality
    4. Confidence intervals
    5. Simple and compound hypotheses, Neyman-Pearson Lemma, uniformly most powerful tests
    6. Likelihood ratio tests
    7. Gauss-Markov theorem
    8. Exponential family
    9. Permutation tests
    10. Delta Method
    11. Rao-Blackwell Theorem
  3. Biostatistical Methods I
    1. Data types and scales
    2. Graphs and tables
    3. Descriptive statistics
    4. Confounding
    5. Probability laws
    6. Bayes’ Theorem
    7. Random variables and expectations
    8. Discrete and continuous distributions
    9. Sampling distributions
    10. Estimation and confidence intervals
    11. Hypothesis testing
    12. 1-Sample and 2-Sample Techniques
    13. F-tests, t-tests, and chi-square tests
    14. Nonparametric tests
    15. One-way ANOVA
    16. Regression concepts
  4. Biostatistical Methods II
    1. Linear regression
      • Matrix formulation
      • Least squares and maximum likelihood estimation
      • Inference
      • Model selection and diagnostics
  5. Analysis of variance (ANOVA)
    • Single and multifactor models
    • Random and fixed effects
    • Crossed and nested factors
    • Sums of squares, mean squares, and expected mean squares
    • Multiple comparisons
  6. Sample size and power considerations
  • Biostatistical Methods in Categorical Data
    1. Prevalence and incidence, calculation of exposure time
    2. Relative risk and odds ratio
    3. Effect modification and confounding
    4. Adjustment of data using stratification
    5. Contingency tables
    6. Case-control study
    7. Logistic regression
    8. Generalized Linear Models (GLM)
    9. Receiver operating characteristic (ROC) analyses
    10. Poisson regression
    11. Sample size


Degree Requirements

Although MPH students completing the Biostatistics-Quantitative Methods (MPH-QM) subtrack are part of the Biostatistics student body, the MPH degree is administered through the office of the Director of the MPH Program, Dr. Anjali Deshpande. MPH students should be aware of the following:

MPH students have their own student handbook and are held accountable for the policies and procedures stated in that handbook. MPH-QM students may find the Biostatistics student handbook a helpful resource, but should not consider that its policies pertain to them.
The practicum experience is the culminating requirement of the MPH degree. Students should begin to plan for the practicum and meet with the Practicum advisor early in their program.

M.P.H. in Quantitative Methods Compared to the M.S. in Biostatistics

Ph.D. in Biostatistics

Degree Requirements

Ph.D. Comprehensive Examination

See also Section XII K. in the Manual of Rules and Regulations of the Graduate College. The Ph.D. comprehensive examination is administered by the departmental Ph.D. Comprehensive Examination Steering Committee. The examination consists of two parts, an in-class component and a take-home component.

The Ph.D. Comprehensive Examination Steering Committee will be comprised of at least five faculty members from the Department of Biostatistics. The committee is responsible for (1) assigning individual sections of the exam to the faculty members who will write the problems for these sections, (2) reviewing the exam sections and recommending revisions (if warranted), (3) approving the final version of the exam, and (4) providing feedback, and possibly recommendations, to the faculty once the exam has been administered and graded. The faculty members who write the problems for the individual exam sections will comprise the Ph.D. Comprehensive Examination Committee. The problems on a section for a course will typically be written by the most recent course instructor; otherwise, the problems will be reviewed by the most recent instructor.

In-Class Component

The in-class component is a two-day in-class exam; each day is three hours in length. The in-class examination will be comprised of four courses. The student will choose three of the courses for the examination. At least one of these courses must be selected from among Theory of Biostatistics II and Linear Models. Additional courses will be chosen from the remaining three core courses and approved electives.

The structure of the exam is outlined below.

  • The following core course is required for all students:
    • Theory of Biostatistics I
  • At least one of the remaining courses may be selected from among the following core theoretical courses:
    • Theory of Biostatistics II
    • Linear Models
  • The remaining course (or courses) may be selected from among the following core and elective courses:
    • Survival Data Analysis
    • Longitudinal Data Analysis
    • Analysis of Categorical Data
    • Bayesian Methods and Design
    • Advanced Clinical Trials
    • Time Series Analysis
    • Advanced Biostatistics Seminar (when appropriate)

Other doctoral level courses in statistics or biostatistics can serve as examination electives, provided that (1) the student obtains the approval of his/her advisor and the Director of Graduate Studies (DGS), and (2) a faculty member is available to write problems for the examination component.

Students are encouraged to discuss course selections with their advisors. Course selections must be approved by the advisor as well as the DGS. If a student does not pass the examination on the first attempt, the student is allowed to change his/her selections when the examination is retaken. However, the selections must again be approved by the advisor as well as the DGS.

Take-Home Component

    • The Take-Home examination will be comprised of three sections:
      • Data Analytic Problem
      • Simulation Problem
      • Open Problem
    • The student will choose the topic for the Data Analytic Problem from among the following courses:
      • Survival Data Analysis
      • Longitudinal Data Analysis
      • Analysis of Categorical Data
      • Bayesian Methods and Design
      • Advanced Clinical Trials
      • Time Series Analysis
      • Advanced Biostatistics Seminar (when appropriate)
    • It is expected that each section can be written and completed in one day (i.e., six to eight hours of work).
    • The Simulation Problem will involve some programming, generally in R, and will be focused on an investigation with practical implications.
    • The Open Problem will consist of a challenging applied problem (e.g., a sample size computation), a problem that is on the periphery of the material the student might encounter in his/her coursework (e.g., principal components regression, ridge regression, the false discovery rate, instrumental variables, model selection criteria, etc.), a problem that requires some independent exploration (e.g., a question regarding a recently published result), etc.
    • The Simulation Problem and Open Problem will be written so that every student will have the appropriate background to complete the section.

The student should meet with his/her advisor by the end of the spring semester prior to the PhD Comprehensive Examination to discuss the topic selections and complete the “Course Approval Request” form. This form should be submitted to the Graduate Program Administrator by the end of the spring semester prior to the examination.

Grading Policy for the Ph.D. Comprehensive Examination

The examination results will be summarized and reported to the entire faculty for discussion and evaluation. Students’ identities will be blinded during grading and the initial faculty discussion. For each student, the faculty will determine whether the performance constitutes a passing performance, a failing performance, or a borderline performance. For any student deemed to have a borderline performance, the blind will be removed, and additional information regarding the student’s academic performance may be discussed and taken into account before a decision is reached (e.g., grades in coursework, prior research experience, etc.).

One decision will be made based on the overall performance on both the in-class and take-home components. An overall result of Satisfactory will be reported to the Graduate College if the performance is ultimately determined to be passing; an overall result of Unsatisfactory will be reported if the performance is judged to failing. Otherwise, an overall result of Reservations will be reported to the Graduate College, along with specific remedial measures to address the faculty concerns. These measures may include (yet are not limited to) retaking one component of the exam or retaking one or more courses. The students will be required to complete these remedial requirements before a specified deadline.

The steering committee members will sign the required Graduate College examination reporting form consistent with the departmental vote.

If a student fails the exam, he/she must retake both exam components the following year. A second failure generally leads to dismissal from the doctoral program.

The Ph.D. Comprehensive Examination is offered once yearly. Copies of past written exams are available for review in the shared network folder: U:\Shared by All\Biost PhD Comprehensive Exams. Any student who has a disability that may require some modification of seating or testing must inform the Biostatistics Graduate Program Administrator when intent is declared to take the examination.

Outline of possible topics covered by the Ph.D. comprehensive examination

BIOS:7110 Theory of Biostatistics I

Primary Reading List

  • BIOS:7110 Course Notes
  • Lehmann and Casella (1998). Theory of Point Estimation. Chapters 1,2,6.

Supplementary Reading List

  • Bickel and Doksum (2001). Mathematical Statistics. Chapters 1-6.
  • Cox and Hinkley (1974). Theoretical Statistics. Chapters 2,3,4,7,8.
  • Ferguson (1996). A Course in Large Sample Theory.
  • Serfling (1980). Approximation Theorems for Mathematical Statistics.
  • Rohatgi (1976). Introduction to Probability Theory and Mathematical Statistics. Chapter 6.

Topics List

  1. Probability theory (CN,BD)
    1. Conditional expectation
    2. Distribution theory of transformations
    3. Multivariate normal distribution
  2. Statistical models (CN,L,BD,CH)
    1. Sufficiency
    2. Exponential family
    3. Frequentist and Bayesian models
    4. Parameter space types: parametric, nonparametric, semiparametric
  3. Methods of estimation (CN,L,BD,CH)
    1. Substitution methods, including method of moments
    2. Least squares methods
    3. Maximum likelihood estimates and Newton-Raphson algorithm
    4. Bayesian estimators
  4. Optimal Estimation (CN,L,BD,CH)
    1. Criteria of estimation
    2. Uniformly minimum variance unbiased estimators
    3. Information inequality (Cramer-Rao)
  5. Basic asymptotic theory (CN,R,F,S)
    1. Modes of convergence; probability inequalities
    2. Laws of large numbers
    3. Continuous mapping theorem
    4. Central limit theorems: Levy, Lindeberg-Feller
    5. Distributions of transformed sequences: Taylor series, approximations to moments, delta method, variance stabilizing transform
  6. Asymptotic likelihood theory and methods (CN,L,S)
    1. Estimation; consistency, asymptotic normality, efficiency
    2. Testing and confidence intervals: score, Wald and likelihood ratio tests with and without nuisance parameters
    3. Reparametrization


BIOS:7120 Theory of Biostatistics II

Primary Reading List

  • Course Notes

Supplementary Reading List

  • Articles referenced in class
  • McCullagh and Nelder (1989). Generalized Linear Models. Chapters 2,4,7,9.
  • Little and Rubin (2002). Statistical Analysis with Missing Data. Chapters 7,9.
  • Lehmann and Romano (2005). Testing Statistical Hypotheses. Chapters 4,5.
  • Cox and Hinkley (1974). Theoretical Statistics. Section 5.2, Chapter 6.


Topics List

  1. Generalized linear models
    1. Framework of a GLM
    2. Parameter estimation
    3. Full likelihood with applications to logistic and Poisson regression
  2. Model and data problems
    1. MLEs and variance estimation for misspecified models
    2. Missing data classification: MCAR, MAR, MNAR
    3. ML and Bayes estimators with missing data
  3. Nuisance parameters
    1. Similar tests and Neyman structure
    2. Permutation tests
    3. Randomization and population models
  4. Some extensions of likelihood theory
    1. Profile likelihood
    2. Conditional likelihood with applications to conditional logistic regression
    3. Generalized linear mixed models: Framework, modeling and likelihood
  5. Estimating equations
    1. Quasi-likelihood with application to overdispersion
    2. Generalized estimating equations
  6. The EM Algorithm
    1. The algorithm
    2. Missing data
    3. Theory of EM algorithm; EM for exponential family
    4. Missing information principle
    5. Standard error estimation for the EM

BIOS:7210 Survival Data Analysis

Reading List

  • BIOS:7210 Course Notes
  • Kalbfleisch and Prentice (2002). The Statistical Analysis of Failure Time Data.
  • Klein and Moeschberger (2003). Survival Analysis for Censored and Truncated Data.
  • Cox and Oakes (1984). Analysis of Survival Data.

Topics List

  1. Functions of survival time
    1. Survival, hazard and density functions
    2. Continuous and discrete versions
  2. Censoring and truncation
    1. Right, left and interval censoring
    2. Left and right truncation
    3. Likelihood construction for censored and truncated data
  3. Parametric survival analysis
    1. Classical survival models (exponential, Weibull, lognormal, log-logistic)
    2. Accelerated failure time model
    3. Proportional hazards model
    4. Newton-Raphson algorithm
    5. Score, Wald and likelihood ratio tests with and without nuisance parameters
  4. Nonparametric survival analysis
    1. Product Limit estimator and Nelson-Aalen estimator
    2. Two-sample weighted logrank tests
    3. Sample size calculation for the logrank test*
    4. K-sample tests
    5. Stratified tests
  5. Cox regression
    1. Proportional hazard regression model
    2. Partial likelihood; tied failure times
    3. Estimation and testing
    4. Estimation of the survival function under the PH model*
    5. Stratification of the Cox model
    6. Regression diagnostics*
    7. Robust variance estimator
    8. Analysis with time-dependent covariates
    9. Checking the proportional hazards assumption
  6. Competing risks
    1. Challenges of competing risk data
    2. Cumulative incidence function

* Note: Some of these topics are more appropriate for a takehome exam rather than an in-class exam.  They include 4.c, 5.d, 5.f.


BIOS:7310 Longitudinal Data Analysis

Reading List

  • BIOS:7310 Course Notes
  • Davis (2002). Statistical Methods for the Analysis of Repeated Measurements. This is the primary reference for classical approaches to longitudinal data, covered mostly in the first part of the course.
  • Verbeke and Molenberghs (2001). Linear Mixed Models for Longitudinal Data, Springer Series in Statistics.  This is a good source reference for the linear mixed model.
  • Molenberghs and Verbeke (2005). Models for Discrete Longitudinal Data. This is a good source reference for the non-Gaussian section of the course.
  • Fitzmaurice, Laird, and Ware (2002). Applied Longitudinal Analysis. This is actually a lower-level text on this general topic.  It includes some nice discussions on modeling issues.

Course Outline

  1. Historical Perspectives and Response Feature Methods
    1. Pros and cons of collapsing longitudinal measures into a single summary measure.
    2. Impact on error rates
  2. Terminology
    1. split plot, repeated measures, cross-over designs
    2. distinction from time series data
    3. transition modeling
    4. subject-specific versus population-averaged
    5. cluster-specific , cluster-varying, time-invariant, time-stationary, time varying covariates
  3. Repeated Measures ANOVA
    1. Typical and generalized models and assumptions
    2. Definition of sphericity
    3. Role of randomization in meeting the sphericity assumption
    4. Mauchly’s test of sphericity
    5. Impact of sphericity on F tests
    6. Degrees of freedom adjustments for non-sphericity
    7. Programming in SAS
  4. Linear Model with multivariate normal responses
    1. Review of matrix notation and linear regression model
    2. Overview of MANOVA modeling and assumptions
    3. Modeling with fixed effects and a pre-specified covariance structure
    4. Types of covariance structures
    5. Programming in SAS
  5. REML estimation
    1. Definition
    2. Comparison to ML estimation
    3. Programming in SAS
  6. Mean and Covariance Model Comparisons without Random Effects
    1. Nesting of models
    2. Likelihood Ratio Tests (LRTs)
    3. Information Criteria (AIC, AICC, BIC)
    4. Impact of over- and under-specifying the covariance structure
  7. Modeling Issues
    1. Difference from baseline versus baseline as covariate
    2. Piecewise linear models
  8. Gaussian Mixed Model in the Longitudinal Data Setting
    1. Model notation and assumptions
    2. Conditional versus marginal moments and interpretations
    3. Maximizing the marginal likelihood; impact on interpretation of covariance parameters.
    4. Hierarchical modeling
    5. Issues with LRT comparisons of covariance structures / Mixture of chi-square distributions
    6. Asymptotic tests of mean parameters; approximate F tests; degrees of freedom issue
    7. Programming in SAS
  9. Review of generalized linear model (GLM) (Covered in 171:203)
    1. Notation
    2. Interpretation of parameters
    3. Offsets, over-and under-dispersed
  10. Generalized Estimating Equations
    1. Motivation and definition
    2. Quasi-likelihood
    3. Starting values
    4. Working correlation/covariance
    5. Asymptotic tests of mean parameters
    6. Interpretation of parameters
    7. Impact of misspecified working covariance on parameters and their standard errors
    8. Naïve (empirical) and robust (sandwich) estimators of covariances
    9. Impact of over- and under-specifying the structure of the working covariance
    10. Estimation issues
    11. Model selection measures (QIC, QICu, CIC)
  11. Generalized Linear Mixed Models
    1. Theoretical model and assumptions
    2. Interpretations of mean () parameters
    3. Comparison to estimates obtained through GEE
    4. Estimation methods used, strengths/weaknesses
    5. Asymptotic tests of mean parameters
    6. Approaches to model selection and residual graphs
  12. Choice of appropriate method(s) – throughout the class, as more options are discussed


BIOS:7410 Analysis of Categorical Data

Reading List

  • BIOS:7410 Course Notes
  • Agresti (2002). Categorical Data Analysis (Second Edition). (Required)
  • Agresti (2007). An Introduction to Categorical Data Analysis (Second Edition).
  • Dobson (2001). An Introduction to Generalized Linear Models (Second Edition).

Topic List

  1. Distributions and Inference for Categorical Data (Chapter 1)
    1. Categorical Response Data
    2. Common Study Designs
    3. Binomial, Poisson, and Multinomial Distributions
    4. Overdispersion / Negative Binomial Distribution
    5. Likelihood Functions / Maximum Likelihood Estimation
    6. Wald, Score, Likelihood Ratio (LR) Tests
    7. Confidence Intervals Based on Test Inversion
    8. Inference for Binomial Parameters
    9. Inference for Multinomial Parameters / Pearson’s and LR Chi-Squared Tests
  2. Analysis of Contingency Tables (Chapters 2 and 3)
    1. Sampling and Probability Distribution Models
    2. Relative Risk, Odds Ratio, and Measures of Association for 2 × 2 Tables
    3. Conditional and Marginal Associations in Three-Way Tables
    4. Odds Ratios and Measures of Association for I × J Tables
    5. Confidence Intervals for Association Measures
    6. Testing Independence in Two-Way Tables
    7. Pearson Residuals
    8. Partitioning Chi-Squared Test Statistics
    9. Two-Way Tables Based on Ordinal Variables
    10. Fisher’s Exact Test / Exact Tests of Independence for I × J Tables
  3. Generalized Linear Models (GLM’s) (Chapter 4)
    1. Components of the GLM
    2. GLM’s for Binary and Count Data
    3. Moments, Likelihood, and Likelihood Equations for GLM’s
    4. Inference for GLM’s
    5. Deviance / Model Fit / Estimation of Dispersion Parameter
    6. Pearson and Deviance Residuals
    7. Maximum Likelihood / Newton-Raphson / Fisher Scoring
    8. Quasi-Likelihood Estimation
    9. Overdispersed GLM’s and Quasi-Likelihood Estimation
  4. Logistic Regression: Logit Models for Binary Responses (Chapters 5 and 6)
    1. Parameter Interpretation and Model Structure
    2. Logistic Regression with Case-Control Studies
    3. Inference and Model Fit
    4. Categorical Explanatory Variables
    5. Logit Models for I × 2 × K and 2 × 2 × K Tables
    6. Model Selection / Akaike Information Criterion
    7. Measures of Predictive Power / Classification Tables and ROC Curves
  5. Logit Models for Multicategory Responses (Chapter 7)
    1. Nominal Responses and Baseline Category Models
    2. Ordinal Responses / Cumulative Logit Models / Proportional Odds Models
  6. Loglinear Models (Chapter 8)
    1. Loglinear Models for Two-Way and Three-Way Tables *
    2. Conditional and Marginal Associations / Independence Relations *
  7. Generalized Linear Models for Longitudinal Data Analysis
    1. Quasi-Likelihood Estimation for Longitudinal Data
    2. Generalized Estimating Equations (GEE’s)
  8. Generalized Linear Mixed Models and Longitudinal Data Analysis *
    1. Longitudinal Data Structure *
    2. Traditional Linear Mixed Models for Longitudinal Data *
    3. Generalized Linear Mixed Models (GLMM’s) for Longitudinal Data *

Topics marked with an asterisk (*) would be more suitable for the take-home examination than the in-class examination.


STAT:7200 Theory of Linear Models

Reading List

  • STAT:7200 Course Notes
  • Ravishanker and Dey (2002). A First Course in Linear Model Theory.
  • Schott (1996). Matrix Analysis for Statistics.
  • Christensen (2002). Plane Answers to Complex Questions: The Theory of Linear Models.
  • Searle (1971). Linear Models.
  • Graybill (1986). Theory and Application of the Linear Model.

Topics List

  1. Basic matrix algebra including transposes, ranks, determinants, inverses, generalized inverses, eigenvalues and eigenvectors, spectral decompositions, and related topics
  2. Concepts of estimability and identifiability of linear models
  3. Unweighted, weighted and generalized least squares estimation for linear models, including classical unconstrained fixed-effects models. Orthogonal projections, reparameterizations, Gauss-Markov Theorem,  and algebraic and geometric structure of the analysis of variance
  4. Least-squares estimation for constrained linear models
  5. Distributions in linear models, including the multivariate normal; central and noncentral F, chi-square and t distributions
  6. Operations involving quadratic forms, including expectations, variances, covariances, moment-generating functions, and distributions of linear and quadratic forms, independence of quadratic forms, Cochran’s Theorem
  7. Hypothesis testing, confidence intervals and regions, simultaneous inference, i.e., multiple confidence intervals and multiple comparisons

BIOS:6810 Bayesian Methods and Design

Primary Reading

  • BIOS:6810 Course Notes
  • Carlin, B.P. and Louis, T.A. (2009) Bayesian Methods for Data Analysis, Third Edition, Boca Rotan: Chapman & Hall/CRC Press
  • Parmigiani, G. and Inoue, L.Y. (2010) Decision Theory: Principles and Approaches, New York: Wiley & Sons

Topics List

  • Bayesian Concepts
    • Prior Distributions
    • Sampling Distributions
    • Posterior Distributions (Derivation, Summaries, Inference)
    • Full Conditional Distributions
    • Hierarchical Modeling Framework
  • Bayesian Analysis
    • Single and Multi-Parameter Models
    • Model Assessment (Posterior Predictive Checks)
    • Model Selection (DIC, Bayes Factors)
  • Bayesian Design
    • Axiomatic Development of Subjective Probability
    • Bayesian Decision Theory
    • Design of Experiments as a Bayesian Decision Problem
    • Sequential Bayesian Design Procedures
    • Optimal Bayesian Design
  • Bayesian Computing
    • Markov Chain Monte Carlo (MCMC) Algorithms (Gibbs, Metropolis-Hastings, SMCMC, Slice, Rejection)
    • Implementation with the R Programming Language
    • MCMC Performance Evaluation and Tuning
    • MCMC Convergence Assessment


Continuous Registration after Completion of the Comprehensive Examination

A student is required to register each fall and spring semester after passing the Ph.D. comprehensive examination until the degree is awarded. If a student has no courses to take, the student can fulfill this requirement by registering for 1 s.h. Graduate College course GRAD:6002 (000.002) Doctoral Continuous Registration. For details, see Section XII.K. of the Manual of Rules and Regulations of the Graduate College.

Ph.D. Dissertation

Students should refer to the Graduate College website Theses and Dissertations for specifics on Graduate College regulations and resources for preparation of doctoral dissertations.

The final examination (dissertation defense) may not be held until the next session after passing the comprehensive examination; however, a student must pass the final examination no later than five years after passing the comprehensive examination. Failure to meet this deadline will result in reexamination of the student to determine his or her qualifications for taking the final examination.

The goal of the dissertation is to produce a document from which at least one manuscript can be composed that is publishable in a peer-reviewed journal. Original thought is required in the formulation and conduct of the research, although neither original data collection nor data analysis is strictly required. The structure of the dissertation shall be determined by the dissertation committee in accordance with the Graduate College Rules and Regulations. The doctoral dissertation defense is an oral presentation of the purpose, methods, and results of the dissertation research. A specially formed committee will thoroughly examine the student’s area of knowledge associated with the content of the work.

Dissertation costs are the responsibility of the student, including associated costs such as copying.

Dissertation Committee

The student is responsible for obtaining a dissertation advisor. The dissertation advisor should have a primary, secondary, or joint faculty appointment in the Department of Biostatistics. If a secondary faculty has agreed to advise a dissertation, the student should consult the DGS to determine whether a primary faculty member should serve as co-advisor. The student, in collaboration with the dissertation advisor(s), will constitute a dissertation committee consisting of no fewer than five members of the Graduate College faculty.  The committee must include:

  • At least two faculty members whose primary appointment is in the Department of Biostatistics
  • At least one committee member whose primary appointment is outside the Department of Biostatistics
  • At least four committee members must be University of Iowa tenure-track faculty

This dissertation committee must approve the topic area of research and will provide direction during the preparation of the dissertation by participation in the evaluation, revision, and approval of the dissertation prospectus.

Dissertation Prospectus

The dissertation prospectus describes the rationale for the proposed research and outlines its basic components.

When the dissertation research has progressed to the point where the student and dissertation advisor feel comfortable outlining the eventual contents of the dissertation, the student is required to arrange a meeting to present a summary of proposed research to the dissertation committee. This prospectus should include some completed work, some work in preparation, and some planned work. The prospectus meeting must take place after forming the dissertation committee, and at least one semester prior to the dissertation defense.

The prospectus meeting serves two purposes:

  • It provides an opportunity for the student and dissertation advisor to receive feedback, advice, and commentary on the direction of their research from other faculty members.
  • It informs the dissertation committee of the direction of the dissertation research.

Ideally, the meeting results in a consensus among the committee members and the student that the scope of the proposed research is consistent with departmental and university dissertation standards. Unanimous written confirmation of this consensus is required on the Dissertation Prospectus Approval Form.

The primary component of the prospectus is the oral presentation to the committee. However, to prepare the committee for the meeting, students are expected to provide a written document one week in advance of the prospectus meeting. The form of this document is left to the discretion of the student and advisor, and may consist of a short written description of the proposed research, an electronic copy of the slides to be presented at the meeting, or a preliminary version of the thesis itself with early drafts of some chapters and rough outlines of others.

Dissertation Defense

The student schedules a final examination (doctoral dissertation defense) meeting with the committee. The student is required to: a) have met the dissertation prospectus requirement, b) have met all other requirements for graduation, including passing the comprehensive examination, c) submit thesis first deposit in accordance with the Graduate College rules, and d) distribute the written copy of the dissertation to the dissertation committee members no later than two weeks before the scheduled dissertation defense.

During the defense, the dissertation committee will thoroughly examine the student’s knowledge in the content area of the research. Doctoral dissertation defense examinations are open to the public. Members of the University community are free to attend the open portions of the session.

The final examination (dissertation defense) will be evaluated as satisfactory or unsatisfactory. Two unsatisfactory votes will make the committee report unsatisfactory. In case of a report of unsatisfactory in the final examination, the candidate may not present himself or herself for re-examination until the next session or later. The examination may be repeated only once.

The student must deposit a final copy of the dissertation, which has been approved by the dissertation committee, to the Graduate College by its deadline in order to receive the degree.

Time Considerations
Deadlines are set by the Graduate College for scheduling the dissertation defense and for the initial and final deposits of the dissertation to the Graduate College. Refer to the Graduate Program Administrator and/or posted deadlines for a particular academic session. See Office of the Registrar for posted deadlines.

Undergraduate to Graduate Degree

Undergraduate to Graduate Degree

Satisfactory Progress in the M.S. and Ph.D. Programs

Students are expected to make satisfactory progress in earning their graduate degrees. Satisfactory progress is defined by the following criteria.

  • Students must register for courses each fall and spring semester until course requirements are completed. Students who hold assistantships or fellowships must register for a minimum of 9 s.h.
  • Students are expected to complete and submit a plan of study to the Graduate Program Administrator by the end of their second semester.
  • Students must maintain a minimum GPA of 3.3 if receiving financial aid. Failure to maintain a 3.3 GPA may result in the decrease or elimination of financial support. Students who do not receive financial aid are required to maintain a minimum GPA of 3.0.
  • For courses included in the plan of study, students must not receive a grade of C+ or lower in more than 6 s.h. of coursework.
  • Students in the M.S. program are expected to take the M.S. Core Examination at the beginning of their third semester. Exemptions can only be granted by the Director of Graduate Studies.
  • Students who enter the Ph.D. program without an M.S. degree in Statistics or Biostatistics are expected to take the M.S. Core Examination at the beginning of their third semester in the graduate program, and the PhD Comprehensive Examination by the beginning of their seventh semester in the graduate program. Students who enter the Ph.D. program with an M.S. degree in Statistics or Biostatistics (or equivalent training) are expected to complete the Ph.D. Comprehensive Examination by the beginning of their fifth semester in the Ph.D. program. Exemptions can only be granted by the Director of Graduate Studies.
  • Students in the Ph.D. program are expected to complete and present their doctoral prospectus within three semesters after passing the Ph.D. Comprehensive Examination.
  • Students in the M.S. program are expected to complete their degree requirements in two years. Students who enter the Ph.D. program without an M.S. degree in Statistics or Biostatistics are expected to complete their degree requirements in five years. Students who enter the Ph.D. program with an M.S. degree in Statistics or Biostatistics (or equivalent training) are expected to complete their degree requirements in four years.
  • Financial support for students who do not complete their degree requirements within the expected timeline is not guaranteed and is subject to the availability of funding. For the limited circumstances where financial support is continued for doctoral students who exceed the expected timeline by a year or more, the normative level of support will be a quarter-time as opposed to a half-time assistantship.
  • Students are expected to regularly attend departmental seminars and to document their attendance by completing the “sign up” sheet.

In addition, students receiving financial support in the form of an assistantship will be evaluated at the end of every semester by their assistantship supervisor(s). Students must perform satisfactorily in fulfilling their responsibilities. Failure to do so may result in the decrease or elimination of financial support.

At the end of each academic year, current students will meet with their advisors to review a report prepared by the academic advisor to assess the student’s progress, and to document any unfulfilled requirements for maintaining satisfactory progress. This report must be signed by both the advisor and the student, and submitted to the Director of Graduate Studies.
To request exemptions from any of the preceding requirements, a written statement must be submitted by the student to both the academic advisor and the Director of Graduate Studies. This statement must include a written plan for completing the program.

Students who fail to make satisfactory progress will be asked to meet with their academic advisor and the Director of Graduate Studies, to discuss the expectations and requirements for continuing in the program. Requirements will be provided to the student in writing, along with a timeline for fulfilling them. If the requirements are not fulfilled within the specified timeline, the student is subject to dismissal from the program. This decision is based on a majority vote of the Biostatistics faculty.

Student Progress Report; M.S. Degree

Student Progress Report; Ph.D. Degree


Throughout the academic year, biostatisticians and statisticians from academia and industry are invited to present research seminars in the department. The Biostatistics seminars are normally scheduled twice monthly on Mondays from 3:30-4:30 p.m. These seminars provide an excellent opportunity for students to meet and network with leaders in the field and learn about current research. Biostatistics students are expected to attend the Biostatistics seminars. Seminar announcements are emailed to Biostatistics students and faculty.

The Department of Statistics and Actuarial Science Colloquium is scheduled on Thursdays at 3:30 p.m. in Schaeffer Hall during the academic year. Many of the topics covered in these colloquia are of interest to biostatisticians as well.

General Information for Students

Computer Lab

The Department of Biostatistics computer lab in C310-CPHB is available for use by Biostatistics students when it is not in use for a class. Students are assigned College of Public Health computer accounts at orientation, and will be given 24/7 electronic access for evening and weekend access to the computer lab. Food or drink is not allowed in the computing lab.

Other university computer labs (ITCs) are available throughout campus, including one at nearby Hardin Library. A complete list of available ITCs can be obtained through the University’s Information Technology Services office. A variety of software applications are available to you via the Virtual Desktop at the University of Iowa.

Scan to Email

The College of Public Health has two “scan to e-mail” stations which are located in S206 and S207. These stations will allow you to scan a document directly to your e-mail account. Information on regarding use of this email technology is located on the CPH IT Support website. You may also scan documents to your email from the copier/scanner in Room N335 in the Department of Biostatistics.


Printers are available for student use in the Biostatistics Computing Lab (C310), in the east wing (N321) and one on the west wing (N374) of the Biostatistics Department. Students are given a printing allowance per semester (currently free print credit of $10 per semester). Anything above and beyond the allowance will be charged to the student’s University Bill (U-Bill). Black and white printing is $0.05 per print side. Color printing is $0.50 per print side. The six locations in CPHB include both black and white, as well as color laser printing. The ITC Student Printing service is a campus-wide/enterprise service. In other words, students can print to any ITC on campus and their account will remain the same, such as the Main Library, Hardin Library and the Iowa Memorial Union. For printing/supplies for your assistantship, please contact your supervisor.

Web Print Release Stations in CPHB

There are two Web Print Release Stations, which are located in S206 and S207. These stations will allow you to print from your personal laptop or home computer (with internet connectivity). Here are further instructions — Once you print from your wireless laptop or home system, the print job is not released (and or charged) until you go to the Web Print Release Station (located in S206 or S207) and release it.

Confidential Resources

Desk Space

Limited space is available for graduate students either working as graduate assistants or on a dissertation. Priority is given for students who are graduate research assistants or teaching assistants. Graduate students with office space elsewhere on campus will only be given space if available. Desk allocations are reviewed each semester and are renewed in August. However, designated space can be reassigned at any time as needed or if space is unused.


Every student must activate their university e-mail account upon enrollment. The student will be connected to the College of Public Health network individually and included in the Biostatistics Student Group e-mail distribution list. Via email, students receive information such as seminar announcements, job announcements, program information, etc. E-mail messages should be checked regularly.

Job and Internship Announcements

Announcements of employment and internship opportunities are communicated to students via e-mail. Recent employment opportunities are posted on the Biostatistics website under “Alumni”.

Travel Funds

Each year the department earmarks limited funds for student travel to meetings and conferences. Requests for funding should be addressed to the Director of Graduate Studies, and should include information about the meeting and its URL, the reason for attending (for instance, a poster or oral presentation), and an itemization of funding requested.

Course Descriptions

Also recommended for PhD candidates:

Technical Writing: RHET:7900 (010:550) Special Project for Graduate Students: Writing in the Disciplines, 3 s.h.
This course prepares graduate students for the tasks of writing in their home disciplines and moving between different rhetorical registers in their professional writing. The course addresses a variety of genres germane to graduate writing (cover letters, dissertation chapters, grant proposals, etc.) while making room for more discipline-specific forms such as posters and websites. We will discuss the practice of communicating the same information in different forms and styles as well as the particular limits and advantages of doing so.
The course fosters cross-disciplinary conversations about writing conventions and practices while refining discipline-specific writing strategies and pedagogy. Students will pursue pragmatic, tangible outcomes over the course of the semester. Students should be prepared to bring ongoing writing projects to class, as time will be devoted to workshopping student writing.

Questions or comments? Contact CPH Webmaster. This page was last reviewed on August 10, 2017.