Degree Requirements
The Graduate Program in Biostatistics emphasizes the application of statistics and mathematics to health sciences, including health services delivery, epidemiology, and medicine. The program offers Master of Science, Master of Public Health, and Doctor of Philosophy degrees and is academically supported by the faculty of the Department of Biostatistics.
This Program of Study contains all the pertinent information on course and examination requirements for the M.S., MPH and Ph.D. degrees. It also includes complete course descriptions for required courses and electives.
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Master of Science
A full-time student must register for at least 10 credits during Autumn, Winter, and Spring Quarters. A full summer quarter load is 2 credits. A typical student should complete the Master's degree in two years.
Required Coursework
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Autumn | Winter | Spring | Summer |
|---|---|---|---|---|
| First Year | BIOST 514 STAT 512 BIOST580 |
BIOST 515 STAT 513 BIOST 580 |
Optional course Optional course BIOST 580 |
BIOST 600,700 or elective |
| Second Year | BIOST 536 BIOST 700 Optional Course BIOST 580 |
BIOST 537 BIOST 700 Optional Course BIOST 580 |
BIOST 700 Optional Course Optional Course BIOST 580 |
|
| * BIOST 533, offered each Spring Quarter, is strongly recommended unless student has studied the material previously. | ||||
Students must earn a minimum 3.0 grade in each of the required courses. At the faculty's discretion, qualifying exam performance may outweigh a course grade below the minimum. (Note: The minimum grade requirement for elective courses is 2.7.)
Twelve required elective credits must be taken, at least six credits from Elective List One (methodology emphasis) and six credits from Elective List Two (biology or public health emphasis). Students interested in taking elective courses not listed should get pre-approval from the Graduate Program Coordinator.
Additional Requirements
In addition to the courses above, students in the master's program must write a thesis, take a consulting class (Biostat590**), demonstrate proficiency in a computer language, and pass the First-Year Theory Exam at the master's level (see below).
**Students taking Biostat 590 need to take the Statistics Department's course number 598 (Techniques in Statistical Consulting, a lecture series) before or during the quarter in which they take Biostat 590. Stat 598 is offered only in Autumn and Spring quarters.
Master's Thesis
The Graduate Program in Biostatistics requires that students enroll for a minimum of 18 credits of BIOST 700, Master's thesis credits.
Computer Proficiency
Computer programming is an important skill for statisticians, who frequently must implement estimators not available in standard software or perform simulations to evaluate and compare alternative methods. The department requires a basic level of computing proficiency from all graduates, but encourages them to take the opportunity to gain greater expertise with a variety of computing tools.
The computing proficiency requirement is met when a student writes and documents a computer program sophisticated enough to demonstrate the necessary basic competence in programming, or completes an approved programming course. The student's faculty advisor can approve the proficiency requirement or refer the matter to an ad hoc faculty committee for approval. Examples of a suitable programs might include implementing a new estimator, performing a thorough simulation study, or producing a power calculator for a complex study design.
A sufficiently sophisticated program in any programming language is in principle acceptable, though students are strongly encouraged to take the opportunity to learn C and/or Fortran. Mere proficiency in the use of a statistics package is not sufficient.
Approved courses include:
- STAT/BIOST 534-5 Statistical computing
- CSE 142 Introduction to Programming (also offered through UW Extension)
- MEDED 531 Computing Concepts for Medical Informatics
Examination Requirement
Master's level students take an M.S. Statistical Theory Examination in June at the end of the year they take STAT 512 and 513. This exam covers topics in STAT 512 and 513. If the examination is failed, it must be retaken and passed the succeeding year.
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Master of Public Health (MPH)
To be considered for admission to the Biostatistics MPH pathway, a candidate must hold a doctoral level degree in another field (e.g. M.D., Ph.D., J.D.) or be currently working on such a doctoral degree. Candidates who have not yet been awarded a doctoral degree will not be awarded the Biostatistics pathway MPH until they are awarded their doctoral degree.
The program is viewed to provide quantitative research training to such individuals.
Required Coursework
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| Biostat electives to total 6 credits ** Biostat MPH practicum for 3 credits 63 credits total required |
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*must be taken for a grade **any approved Biostatistics MS elective, or STAT 512 or STAT 513
MPH students must also complete a practicum experience in an organization or agency that provides planning or services relevant to public health.
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Doctor of Philosophy
Students earning the Ph.D. degree develop statistical theory and applications particular to the health sciences. Epidemiology and Genetics are popular minor areas, but other minors are encouraged also. A full-time student must register for at least 10 credits during Autumn, Winter, and Spring Quarters. A full summer load is 2 credits. A strong Ph.D. student will complete our program in four years. Departmental support is not typically available for more than five years.
Required Coursework
| Autumn | Winter | Spring | Summer | |
|---|---|---|---|---|
| First Year | BIOST 514 STAT 512* BIOST 580 Advanced Math** |
BIOST 515 STAT 513* BIOST 580 Advanced Math** |
BIOST 533 BIOST 580 Advanced Math** |
BIOST 600 or Elective |
| Second Year | BIOST 570 STAT 581 BIOST 580 |
BIOST 571 STAT 582 BIOST 580 |
Elective*** STAT 583 BIOST 580 |
BIOST 600 or Elective |
| Third Year | Elective BIOST 580 BIOST 800 |
Elective BIOST 580 BIOST 800 |
Elective BIOST 580 BIOST 800 |
BIOST 600 or Elective BIOST 800 |
| * | Entering students who have studied the topics of STAT 512-513 at another university are encouraged to "place out" of these courses and enroll in STAT 581-582-583 instead. (This placement exam is offered in September during orientation week.) |
| ** | Math 574-575-576 or equivalent. Students are strongly encouraged to take Math 574, 575, and 576 which would prepare them for the 580's. |
| *** | Biostat 572 is strongly encouraged. |
Students must earn a minimum 3.0 grade in each of the required courses. At the faculty's discretion, qualifying exam performance may outweigh a course grade below the minimum. (Note: The minimum grade requirement for elective courses is 2.7.)
Additional Requirements
In addition to the above courses, students in the Ph.D. program must complete 36 credits of BIOST 800, write a dissertation, complete a consulting class, and demonstrate proficiency in a computer language. If a Ph.D. student enrolls in STAT 512 and STAT 513, he or she must take the M.S. Theory Exam in the following June for advisory purposes. Ph.D. students must also take the Ph.D. Statistical Theory exam during the summer following enrollment in STAT 581-2-3 and the Ph.D. Applied exam during the summer following enrollment in BIOST 571 and 572, or in BIOST 570 and 571, or if both STAT 581-2-3 and BIOST 570 and 571 are taken in the same year the student may elect to delay the Ph.D. Applied exam by one year. If either exam is failed, it must be passed the following year. In addition, they must successfully complete six elective credits from List 1 and nine elective credits from List 2. Students in the Ph.D. program must also complete a Biology Project, and pass the General and Final Examinations.
Ph.D. Dissertation
The Graduate Program in Biostatistics requires that students enroll for a minimum of 36 credits of BIOST 800, Ph.D. dissertation credits. Students typically identify a dissertation topic sometime after they have completed their Ph.D. exams. A student requests the appointment of a Supervisory Committee at least four months prior to the General Examination (see below). Usually the Supervisory Committee will be appointed much earlier for the purpose of reviewing the Biology Project. The Supervisory Committee consists of the thesis advisor, chosen by the student, and three or four committee members, including one from the University at large.
Research Experience
Research experience prior to the dissertation is an important part of the program and so students are encouraged to identify an individual research project early in their academic program which may or may not be the same as their dissertation topic. One way to gain this experience is through a research assistantship which allows students to collaborate with investigators in appropriate medical or biostatistical areas. Students can earn credit for this research by registering for BIOST 600.
Computer Proficiency
Computer programming is an important skill for statisticians, who frequently must implement estimators not available in standard software or perform simulations to evaluate and compare alternative methods. The department requires a basic level of computing proficiency from all graduates, but encourages them to take the opportunity to gain greater expertise with a variety of computing tools.
The computing proficiency requirement is met when a student writes and documents a computer program sophisticated enough to demonstrate the necessary basic competence in programming, or completes an approved programming course. The student's faculty advisor can approve the proficiency requirement or refer the matter to an ad hoc faculty committee for approval. Examples of a suitable programs might include implementing a new estimator, performing a thorough simulation study, or producing a power calculator for a complex study design.
A sufficiently sophisticated program in any programming language is in principle acceptable, though students are strongly encouraged to take the opportunity to learn C and/or Fortran. Mere proficiency in the use of a statistics package is not sufficient.
Approved courses include:
- STAT/BIOST 534-5 Statistical computing
- CSE 142 Introduction to Programming (also offered through UW Extension)
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Examinations Requirement
M.S. Level Statistical Theory Exam
If a Ph.D. student enrolls in STAT 512-13, he/she must take the same exam as the M.S. students in June following the courses. For Ph.D. students this exam is for advisory purposes with respect to the decision to proceed to the more advanced courses and the Ph.D. qualifying examinations. Ph.D. students who do not enroll in STAT 512-513 are not required to take this exam.
Qualifying Examinations
Ph.D. students must take an Advanced Statistical Theory Examination in August following the year in which they enroll in STAT 581-2-3. This is a closed book examination, usually of 3 or 4 hours duration. Ph.D. Students must also take an Applied Statistics Examination in the summer following the year in which they enroll in BIOST/STAT 570-1-2. This exam consists of the student analyzing a data set and preparing a written report on a take-home basis during one week, followed by a 30 minute oral exam on his/her analysis. A full time Ph.D. student is expected to take and pass both qualifying exams at the end of the second year. If a qualifying exam is failed it must be successfully retaken the following year. No exam may be taken more than twice.
Ph.D. students in the program may earn a non-thesis master's by successfully completing the first- and second-year (Ph.D.) examinations, all of the second-year coursework, and after applying for a non-thesis MS warrant through the Graduate School.
Biology Project
As soon as she or he feels ready, a student arranges to prepare a Biology Project. The purpose of this project is to demonstrate that the student can digest information about scientific methods, principles, and mechanisms. The project should focus on basic life sciences rather than epidemiology and public health issues; in rare cases a basic-science topic in a related field will qualify. The student chooses a life sciences topic to study and selects an expert in this area to serve as her/his Biology Advisor. The Graduate Program Coordinator must approve this topic. The student can then expect 4-6 weeks of reading and study under the director of this Advisor. The product of this study is a report by the student synthesizing results of this research. The report is usually a one-hour oral presentation and question-and-answer before the student's Biology Exam Supervisory Committee, though the report may instead be written, as agreed upon by the student's Biology Exam Supervisory Committee. The Supervisory Committee decides whether the student has performed satisfactorily.
Biology Exam Supervisory Committee
If the student has already formed her/his supervisory committee for the dissertation, this committee, together with the Biology Advisor, will serve as the supervisory committee for the biology project. If the dissertation supervisory committee has not yet been formed, the Biology Project supervisory committee must consist of at least three members of the graduate faculty from the department of Biostatistics, and the Biology Advisor.
Timing
The biology project must be satisfactorily completed before the student is declared to have passed her/his General Examination.
Waivers
The Biology Project requirement is waived for any student holding a Master's degree (or higher) in a discipline of the life sciences.
General Examination
The General Examination consists of a discussion by the student of a proposed thesis topic. The student is expected to have prepared a dissertation proposal and to be ready to discuss the background of the general topic area. At this time, a student's Committee or other faculty members may also raise questions pertinent to the statistics or mathematics required to implement the thesis proposal. After passing the General Examination, a student should devote most of his or her time to a dissertation research program. (Other comments regarding the General Examination are found in the University's General Catalog and the PhD Checklist, which is available from the student services area.)
Final Examination
The student defends his or her completed dissertation in the final examination. The Ph.D. degree is conferred upon passing this examination.
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Statistical Genetics Pathway
In Autumn 2000 the Departments of Biostatistics and Statistics at the University of Washington, together with colleagues in Genetics, Zoology, Medicine, and Molecular Biotechnology, initiated a new program of graduate study in Statistical Genetics.
The curriculum is intended to serve as a part of the core curriculum for Ph.D. students in Biostatistics and Statistics following the new Statistical Genetics pathway in these two programs. Additionally, the same curriculum will lead to a Certificate of Graduate Study in Statistical Genetics, open to any graduate student at UW for whom "this curriculum is not significantly redundant to degree requirements". To this Certificate Program we expect to attract M.S. students in Biostatistics and Statistics, as well as graduate students in other programs at UW, who wish to enhance their education in this rapidly developing area. The individual classes of the Certificate Program are open to all UW students who have met the necessary prerequisites.
Students interested in this program of study in Statistical Genetics should apply to Statistics or Biostatistics in the usual way. At the University of Washington these two departments maintain a close partnership in their graduate programs, and the new Statistical Genetics curriculum is a joint endeavor. Students should apply to whichever program they feel most generally suited to their interests, and should specify clearly an interest in Statistical Genetics. This specification is not binding. You are welcome to specify a tentative or possible interest. Additionally, applicants may send a brief email to Katie Kerr (Biostatistics, katiek@u.washington.edu) , Ellen Wijsman (Biostatistics, wijsman@u.washington.eud) or to Elizabeth Thompson (Statistics, thompson@stat.washington.edu) to insure their files will be followed up on by the Statistical Genetics faculty.
Background Courses
Following is a list of required background courses for the program in statistical genetics. These courses can be waived if a student has completed an equivalent course elsewhere. Several of the classes are typically offered in the summer quarter. For full course descriptions, see the University Course Catalog at: http://www.washington.edu/students/crscat/
A student should have completed the following (or equivalent) courses:
- CSE 142 Computer Programming for Engineers and Scientists I
- GENET 371 Introductory Genetics
- GENET 372 Gene Structure and Function
- GENET 453 Genetics of the Evolutionary Process
- GENET 465 Advanced Human Genetics
- MBT 510 Technologies for Genome Analysis
- STAT 394 Probability I
- STAT 395 Probability II
- STAT 390 Probability and Statistics in Engineering and Science
Core Curriculum in Statistical Genetics
A three-course core sequence in statistical genetics is offered jointly by the departments of Statistics and Biostatistics.
- Fall Quarter: BIOST/STAT 550: Statistical Genetics I (Statistical methods for Mendelian genetic data)
- Winter Quarter: BIOST/STAT 551: Statistical Genetics II (Statistical methods in quantitative genetics)
- Spring Quarter : BIOST/STAT 552: Statistical Genetics III (Statistical methods in medical genetics
In addition, the following classes are required:
- GENET 562 Population Genetics
- MBT 540 Genome Sequence Analysis
- Biostat 580B Seminar in Biostatistics: Statistical Genetics
For more information, please see the Statistical Genetics Web site at: http://depts.washington.edu/statgen/
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Electives - Master's and Ph.D.
Students may also fulfill the electives requirements with courses not listed below. Other courses may be substituted with approval of the Graduate Program Advisor and Graduate Program Coordinator.
Elective List One (Methodologic Emphasis)
The following electives are labelled "MS" for Master's students and "PHD" for Ph.D. students.
A = autumn quarter
W = winter quarter
Sp = spring quarter
S = summer quarter
| MS | PHD | AMATH 351,2 | Quantitative Methods I, II—A, W, Sp, S |
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| MS | PHD | AMATH 584 | Applied Linear Algebra & Introductory Numerical Methods—A (with Math 584) | |
| MS | PHD | AMATH 585,6 | Approximate and Numerical Analysis II, III—A Math 585, W (with Math 585); A Math 586, Sp (with Atms 581/Math 586) | |
| MS | PHD | BIOST 524 | Design of Medical Studies | |
| MS | PHD | BIOST 529 | Sample Survey Techniques | |
| MS | BIOST 533 | Classical Theory of Linear Models | ||
| MS | PHD | BIOST 534,5 | Statistical Computing I,II | |
| PHD | BIOST 536 | Categorical Data Analysis in Epidemiology | ||
| PHD | BIOST 537 | Survival Data Analysis in Epidemiology | ||
| MS | PHD | BIOST 540 | Correlated Data Regression | |
| MS | PHD | BIOST 550 | Statistical Genetics I: Mendelian Traits | |
| MS | PHD | BIOST 551 | Statistical Genetics II: Quantitative Traits | |
| MS | PHD | BIOST 552 | Statistical Genetics III: Medical Genetic Studies | |
| MS | |
BIOST 570 | Advanced Applied Statistics and Linear Models | |
| MS | |
BIOST 571 | Advanced Applied Statistics and Linear Models | |
| MS | PHD | BIOST 572 | Advanced Applied Statistics and Linear Models | |
| MS | PHD | BIOST 573 | Statistical Methods for Categorical Data Survival Data | |
| MS | PHD | BIOST 576 | Statistical Methods for Survival Data | |
| MS | PHD | BIOST 578 | Special Topics in Advanced Biostatistics | |
| MS | PHD | HSERV 522 | Program Evaluation—A | |
| MS | PHD | HSERV 523 | Community Health Assessment—A | |
| MS | |
QMETH 551 | Linear and Integer Programming—A | |
| PHD | STAT 491,2 | Introduction to Stochastic Processes—A (with Math 491) | ||
| PHD | STAT 521,2,3 | Advanced Probability—A Stat 521; W Stat 522; Sp Stat 523 (alternate years) | ||
| PHD | STAT 542 | Multivariate Analysis |
Elective List Two (Biology or Public Health Emphasis)
The following list is for both Master's and Ph.D. students.
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BIOL 472 | Principles in Ecology—A | ||
| EPI 511 | Introduction to Epidemiology—A | |||
| EPI 512,3 | Epidemiologic Methods I,II—A Epi 512; W Epi 513 | |||
| *EPI 514 | Application of Epidemiologic Methods—Sp | |||
| EPI 517 | Methods & Applications of Genetic Epidemiology—(with Phg 511) | |||
| EPI 519 | Epidemiology of Cardiovascular Disease—A | |||
| EPI 520 | Infectious Diseases Epidemiology—W | |||
| EPI 524 | Epidemiologic Studies of Cancer Etiology & Prevention—W | |||
| EPI 529 | Emerging Infections of International Public Health Importance—W (with HSERV 536); in residence odd years; online even years | |||
| EPI 530 | AIDS: A Multidisciplinary Approach—A (with Med 530) | |||
| EPI/HSERV 531 |
Problems in Interntional Health |
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| EPI 532 | Epidemiology of Infectious Diseases of Third-World Importance | |||
| EPI 533 | Pharmacoepidemiology—Sp even years (with Pharm 533) | |||
| EPI 538 | Nutritional Epidemiology—A (with Nutr 538) | |||
| EPI 539 | Research Methods in Developing Countries—W (with HSERV) | |||
| EPI 542 | Clinical Epidemiology—S | |||
| EPI 590 |
Epidemiology of Chonic Infectious Diseases |
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| ENVH 511 | Environmental and Occupational Health—W | |||
| ENVH 535 | Inhalation Toxicology—A | |||
| ENVH 577 | Risk Assessment for Environmental Hazards—A (with CEE 560;PB AF 589) | |||
| GENOME 371/72 | Introductory Genetics—A, W, Sp, S Genome 371; W Genome 372 | |||
| GENOME 453 | Genetics of the Evolutionary Process—A | |||
| GENOME 562 | Population Genetics—Sp | |||
| HSERV 510 | Social and Behavioral Sciences in Public Health | |||
| HSERV 511 | Introduction to Health Services and Public Health—S, A | |||
| HSERV 541 | Topics in Maternal and Child Health I—A | |||
| **HUBIO 512P | Mechanisms in Cell Physiology—A | |||
| **HUBIO 514P | Biochemistry I-A—A | |||
| HUBIO 524P | Biochemistry I-B—W | |||
| MICROM 301 | General Microbiology—A, Sp, S | |||
| P BIO 405,6 | Human Physiology—A, P Bio 405; W, P Bio 406 |
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| PABIO 511 | Pathobiological Frontiers—S | |||
| PABIO 536 | Bioinformatics and Gene Sequence Analysis—A, Sp (with MEBI 536) | |||
| PABIO 540 | Antibiotic Resistance Mechanisms and Their Impact on Public Health | |||
| PABIO 550 | Diseases of Public Health Importance and Strategies for their Control—A | |||
| PABIO 551 |
Biochemistry and Genetics of
Pathogens and Their Hosts |
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| PCEUT 405 | Biopharmaceutics and Pharmacokinetics—W | |||
| PCEUT 406 | Clinical Pharmacokinetics—Sp | |||
| PCEUT 510 | Pharmacokinetics of Drug Interactions—A | |||
| PCEUT 501 | Advanced Pharmacokinetics—Sp | |||
| PCEUT 502 | Advanced Pharmacokinetics Concepts—A, odd years | |||
| PHG 537 | Pharmacoeconomics, Genetics, and Health Care—A |
| * | Course has an enrollment limit. Preferences given to Epi PhD students. Also designed for students who have limited SAS background. |
| ** | To enroll in this course, a student and his or her advisor must request permission in writing. This is subject to approval by the Course Chairman and the Curriculum Committee for the Medical School. |
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Course Descriptions
Biostatistics
Note: If you are looking for the web page of a particular class; try looking at our list of classes.
- BIOST 514: Biostatistics I (4)
- Mathematically sophisticated presentation of principles and methods of data description; graphics; point confidence interval estimation; hypothesis testing; relative risk; odds ratio; Mantel-Haensel; chi-square test (matrix algebra required). Examples drawn from biomedical literature; real data sets analyzed using statistical computer packages. Prerequisite: Biostatistics major or permission of instructor.
- BIOST 515: Biostatistics II (4)
- Mathematically sophisticated introduction to linear models; multiple regression, correlation; residual analysis; dummy variables; analysis of covariance; one-, two-way analysis of variance; randomized blocks; fixed, random effects (repeated measures, factorial designs); multiple comparisons. Matrix algebra required. Real biomedical data sets analyzed. Prerequisites: Biostatistics major; 514 or permission of instructor.
- BIOST 524: Design of Medical Studies (3)
- Design of medical studies, with emphasis on randomized controlled clinical trials. Bias elimination, controls, treatment assignment and randomization, precision, replication, power and sample size calculations, stratification, and ethics. Suitable for graduate students in biostatistics and research-oriented graduate students in other scientific fields. Offered jointly with STAT 524. Prerequisites: BIOST 511 or equivalent and one of BIOST 513, EPI 512, STAT 421, 423, or 512, or permission of instructor.
- BIOST 529: Sample Survey Techniques (3)
- Design and implementation of selection and estimation procedures in sample surveys. Emphasis on the sampling of human populations, although principles apply to other sampling problems. Topics: two-phase procedures, optimal allocation of resources, estimation theory, replicated designs, variance estimation, national samples and census materials. Offered jointly with STAT 529. Prerequisites: BIOST 511 or STAT 421, 423, or permission of instructor.
- BIOST 533: Classical Theory of Linear Models (3)
- Introduction to one-, two-way analysis of variance, randomized blocks; fixed, random effects, multiple comparisons. Statistical distribution theory for quadratic forms of normal variables. Fitting of the general linear model by least squares. Prerequisites: 513, STAT 421, or 423; and STAT 513; and a course in matrix algebra.
- BIOST 534: Statistical Computing I (3)
- Introduction to topics in statistical computing; application of numerical methods to statistical problems; generation of pseudo random numbers; design and execution of Monte Carlo studies; comparative evaluation of statistical algorithms; matrix methods and least squares; computation of probabilities; data structures and data base management. Previously 574. Offered jointly with STAT 534. Prerequisites: STAT 511 and programming, or permission of instructor.
- BIOST 535: Statistical Computing II (3)
- Computational methods in statistics: generation of pseudo random numbers, Monte Carlo Quadrature, variance reduction techniques, design of Monte Carlo studies, nonlinear optimization, nonlinear least square, selected special topics. Previously 575. Offered jointly with STAT 535. Prerequisites: BIOST/STAT 534 or equivalent.
- BIOST 536: Categorical Data Analysis in Epidemiology (4) A
- Introduction to multiple regression of categorical epidemiologic data using multiplicative models. Interpretation and familiarity with available programs gained by analysis of bona fide data; critiques of analyses appearing in literature. Offered jointly with EPI 536. Prerequisites: BIOST 513 and EPI 513; or BIOST 515; or permission of instructor.
- BIOST 537: Survival Data Analysis in Epidemiology (4) WI
- Introduction to the multiple regression analysis of survival data using multiplicative models. Application to epidemiologic studies. Familiarity with interpretation and available computer programs gained by analysis of bona fide sets of data and critiques of analyses appearing in the literature. Prerequisites: BIOST 536 or permission of the instructor.
- BIOST 540: Correlated Data Regression (3)
- Introduction to regression modeling of longitudinal and clustered data from epidemiology and health sciences. Interpretation and familiarity with available programs gained by analysis of bona fide data; critiques of analyses appearing in literature. Prerequisite: Either BIOST 513, BIOST 515, BIOST 518, BIOST 536, or permission of instructor. Offered: Sp.
- BIOST 550: Statistical Genetics I: Mendelian Traits (3)
- Mendelian genetic traits. Population genetics; Hardy-Weinberg, allelic variation, subdivision. Likelihood inference, information and power; latent variables and EM algorithm. Pedigree relationships and gene identity. Meiosis and recombination. Linkage detection. Multipoint linkage analysis. Prerequisite: STAT 390 and STAT 394, or permission of instructor. Offered: jointly with STAT 550; A.
- BIOST 551: Statistical Genetics II: Quantitative Traits (3)
- Statistical basis for describing variation in quantitative traits. Decomposition of trait variation into components representing genes, environment and gene-environment interaction. Methods of mapping and characterizing quantitative trait loci. Prerequisite: STAT/BIOST 550; STAT 423 or BIOST 515; or permission of instructor. Offered: jointly with STAT 551; W.
- BIOST 552: Statistical Genetics III: Design and Analysis (3)
- Overview of probability models, inheritance models, penetrance. Association and linkage. The lod score method. Affected sib method. Fitting complex inheritance models. Design mapping studies; multipoint, disequilibrium, and fine-scale mapping. Ascertainment. Prerequisite: STAT/BIOST 551; GENET 371; or permission of instructor. Offered: jointly with STAT 552; Sp.
- BIOST 570: Advanced Applied Linear Models (3)
- Review of the normal theory least squares approach to multiple linear regression: Estimation, variable selection and residual diagnostics. Relaxation of assumptions: outliers, robust estimation, weighting, re-sampling methods for variance estimation and variable selection. Generalized linear models as a flexible tool for regression analysis in an exponential family framework. Likelihood- based procedures for estimation, diagnostic checking and model selection. Particular emphasis on techniques for analyzing proportion and count data via logistic regression and log-linear models. Illustrations with data from designed and observational experiments in basic science, engineering, medicine and public health. Offered jointly with STAT 570. Prerequisites: STAT 421, 423; or STAT 512, 513; or STAT/BIOSTAT 533 and a course in matrix algebra.
- BIOST 571: Advanced Applied Linear Models (3)
- A brief review of generalized linear model; Overdispersion models; General linear model: fixed, random and mixed effects models; Variance components; EM algorithm; Analysis of Longitudinal data; Univariate and multivariate methods. Two stage model; marginal model; patterned covariance matrix; nonparametric approach based on ranks. Offered jointly with STAT 571. Prerequisites: STAT 513, BIOST/STAT 570, a matrix algebra course, or permission of instructor.
- BIOST 572: Advanced Applied Linear Models (3)
- Empirical model building techniques. Goals compared and contrasted to parametric inference for designed experiments. The equal role of bias and variance. Re-sampling and cross-validation as model selection procedures; pitfalls. Development of basic building blocks: recursive partitioning, transformation by 1-dimensional smoothing, and selection of linear combinations of variables. Application to classification and regression with continuous and discrete response data. Particular emphasis on the refinement of these methods within the framework of Generalized Linear models. Considerations for multivariate response data. Illustrations with data from designed and observational experiments in basic science, engineering, medicine and public health. Offered jointly with STAT 572. Prerequisites: BIOST/STAT 570, 571.
- BIOST 573: Statistical Methods for Categorical Data (3) Sp
- A collection of advanced topics in generalized linear models for categorical data. Subjects treated will include parameters in the link function to check model adequacy, parameters in the variance function to accommodate overdispersion, and joint modelling of mean and variance functions. The course will also cover conditional exact methods and their approximation by saddle point methods. Offered jointly with STAT 573. Prerequisites: 572 and STAT 582 or permission of instructor.
- BIOST 576: Statistical Methods for Survival Data (3)
- Statistical methods for censored survival data arising from follow-up studies on human or animal populations. Parametric and nonparametric methods, Kaplan-Meier survival curve estimator, comparison of survival curves, log-rank test, regression models including the Cox proportional hazards model, competing risks. Offered jointly with STAT 576. Prerequisites: 513, Q SCI 383, STAT 473, or equivalent and STAT 581. (Offered alternate years.)
- BIOST 578: Special Topics in Advanced Biostatistics (1-3)
- Advanced-level topics in biostatistics offered by regular and visiting faculty. Offered jointly with STAT 578. Prerequisites: permission of instructor.
- BIOST 579: Data Analysis and Reporting (2)
- Analysis of real data to answer scientific questions. Common data-analytic problems. Sensible approaches to complex data. Graphical and tabular presentation of results. Writing reports for scientific journals, research collaborators, consulting clients. Graduate standing in statistics or biostatistics or permission of instructor. Offered: jointly with STAT 579; AWSu.
- BIOST 580: Seminar in Biostatistics (*, Max 9)
- Presentation and discussion of special topics and research results in biostatistics. Speakers include resident faculty, visiting scientists, and advanced graduate students.
- BIOST 590: Biostatistical Consulting (*)
- Training in consulting on the biostatistical aspect of research problems arising in the biomedical field. Students, initially under the close supervision of a faculty member, participate in discussions with investigators leading to the design and/or the analysis of a quantitative investigation of a problem. With experience, independent associations of student and research worker are encouraged with subsequent review by faculty of resulting design and analysis. Required of masters and doctoral students. Prerequisite: students need to have sat in on Stat 598 beforehand or during the quarter they plan to take consulting.
- BIOST 593: Cancer Prevention Research
- Research experience for pre and postdoctoral students working on cancer prevention projects at the Fred Hutchinson Cancer Research Center. Offered jointly with EPI 593.
- BIOST 600: Independent Study or Research (*)
- BIOST 700: Master's Thesis (*)
- BIOST 800: Doctoral Dissertation (*)
Statistics
- STAT 491,2: Introduction to Stochastic Processes (3,3)
- Random walks, Markov chains, branching processes, Poisson process, point processes birth and death processes, queuing theory, stationary processes. Offered jointly with MATH 491,2. Prerequisites: 396 for 491; 491 for 492.
- STAT 512,3: Statistical Inference (4,4) A, W
- General theory of statistical inference; estimation and hypothesis testing; multivariate theory; regression, correlation, and analysis of variance. Prerequisites: for 512: 395 (concurrent registration permitted) or 511; and 421, 423, or BIOST 512 (concurrent registration permitted for these three).
- STAT 521,2,3: Advanced Probability (3,3,3) A, W, Sp
- Measure theory and integration, independence, laws of large numbers. Fourier analysis of distributions, central limit problem and infinitely divisible laws, conditional expectations, martingales. Offered jointly with MATH 521,522, 523. Prerequisite: MATH 426.
- STAT 542: Multivariate Analysis (3) Sp
- Multivariate normal distribution; partial and multiple correlation; Hotelling's T2; Bartlett's decomposition; various likelihood ratio tests; discriminant analysis; principal components. Prerequisite: 582 or permission of instructor. (Offered alternate years.)
- STAT 543: Nonparametric Statistics (3)
- Linear rank statistics, asymptotics, ties, test of fit; the Hodges-Lehmann estimator. Nonparametric analysis of variance; Kruskal-Wallis, Friedman, and aligned-rank tests. Prerequisite: 512 or permission of instructor.
- STAT 581,2,3: Advanced Theory of Statistical Inference (3,3,3) A, W, Sp
- Limit theorems, asymptotic efficiency, maximum likelihood statistics; sufficient and ancillary statistics; elements of decision theory, Neyman-Pearson theory, uniformly most powerful unbiased and invariant tests; sequential analysis; distribution-free statistics; linear hypotheses. Prerequisites: 513 and MATH 424, 425, 426 for 581 (concurrent enrollment in MATH 424, 425, 426 is permissible); 570 and 581 for 582; 582 for 583.