Biostatistics (Graduate Group)

Kyoungmi Kim, Ph.D. (Public Health Sciences), Chairperson of the Group

Group Office. 4118 Mathematical Sciences Building; 530-341-2987; http://biostat.ucdavis.edu/

Faculty. http://biostat.ucdavis.edu/about/faculty.html


Kyoungmi Kim, Ph.D. (Public Health Sciences), Chairperson of the Group

Group Office. 4118 Mathematical Sciences Building; 530-341-2987; http://biostat.ucdavis.edu/

Faculty. http://biostat.ucdavis.edu/about/faculty.html

Graduate Study. Biostatistics is a field of science that uses quantitative methods to study life sciences related problems that arise in a broad array of fields. The program provides students with, first, solid training in the biostatistical core disciplines and theory; second, with state-of-the art knowledge and skills for biostatistical data analysis; third, substantial exposure to the biological and epidemiological sciences; and fourth, with a strong background in theoretical modeling, statistical techniques and quantitative as well as computational methods. Programs of study and research are offered leading to the M.S. and Ph.D. degrees. The program prepares students for interdisciplinary careers ranging from bioinformatics, environmental toxicology and stochastic modeling in biology and medicine to clinical trials, drug development, epidemiological and medical statistics. The program draws on the strengths of the Biostatistics faculty at UC Davis.

Preparation. Students should have one year of calculus; a course in linear algebra or one year of biological course work; facility with a programming language; and upper-division work in at least one of Mathematics, Statistics and Biology.

Graduate Advisor. Ana-Maria Iosif (Public Health Sciences)


Kyoungmi Kim, Ph.D. (Public Health Sciences), Chairperson of the Group

Group Office. 4118 Mathematical Sciences Building; 530-341-2987; http://biostat.ucdavis.edu/

Faculty. http://biostat.ucdavis.edu/about/faculty.html

Graduate Study. Biostatistics is a field of science that uses quantitative methods to study life sciences related problems that arise in a broad array of fields. The program provides students with, first, solid training in the biostatistical core disciplines and theory; second, with state-of-the art knowledge and skills for biostatistical data analysis; third, substantial exposure to the biological and epidemiological sciences; and fourth, with a strong background in theoretical modeling, statistical techniques and quantitative as well as computational methods. Programs of study and research are offered leading to the M.S. and Ph.D. degrees. The program prepares students for interdisciplinary careers ranging from bioinformatics, environmental toxicology and stochastic modeling in biology and medicine to clinical trials, drug development, epidemiological and medical statistics. The program draws on the strengths of the Biostatistics faculty at UC Davis.

Preparation. Students should have one year of calculus; a course in linear algebra or one year of biological course work; facility with a programming language; and upper-division work in at least one of Mathematics, Statistics and Biology.

Graduate Advisor. Christiana Drake (Statistics)


Courses in BST:
BST 222Biostatistics: Survival Analysis (4) Active
Lecture—3 hour(s); Discussion/Laboratory—1 hour(s). Prerequisite(s): STA 131C. Incomplete data; life tables; nonparametric methods; parametric methods; accelerated failure time models; proportional hazards models; partial likelihood; advanced topics. (Same course as STA 222.) (Letter.) Effective: 2002 Fall Quarter.
BST 223Biostatistics: Generalized Linear Models (4) Active
Lecture—3 hour(s); Discussion/Laboratory—1 hour(s). Prerequisite(s): STA 131C. Likelihood and linear regression; generalized linear model; Binomial regression; case-control studies; dose-response and bioassay; Poisson regression; Gamma regression; quasi-likelihood models; estimating equations; multivariate GLMs. (Same course as STA 223.) (Letter.) Effective: 2002 Fall Quarter.
BST 224Analysis of Longitudinal Data (4) Active
Lecture—3 hour(s); Discussion/Laboratory—1 hour(s). Prerequisite(s): (BST 222 or STA 222); (BST 223 or STA 223); STA 232B; or Consent of Instructor. Standard and advanced methodology, theory, algorithms, and applications relevant for analysis of repeated measurements and longitudinal data in biostatistical and statistical settings. (Same course as STA 224.) (Letter.) Effective: 2005 Spring Quarter.
BST 225Clinical Trials (4) Active
Lecture—3 hour(s); Discussion/Laboratory—1 hour(s). Prerequisite(s): BST 223 or STA 223; or Consent of Instructor. Basic statistical principles of clinical designs, including bias, randomization, blocking, and masking. Practical applications of widely-used designs, including dose-finding, comparative and cluster randomization designs. Advanced statistical procedures for analysis of data collected in clinical trials. (Same course as STA 225.) (Letter.) Effective: 2005 Spring Quarter.
BST 226Statistical Methods for Bioinformatics (4) Active
Lecture—3 hour(s); Discussion/Laboratory—1 hour(s). Prerequisite(s): BST 131C or Consent of Instructor; Data analysis experience recommended. Standard and advanced statistical methodology, theory, algorithms, and applications relevant to the analysis of -omics data. (Same course as STA 226.) (Letter.) Effective: 2007 Winter Quarter.
BST 227Machine Learning in Genomics (4) Active
Lecture/Discussion—3 hour(s); Project (Term Project). Prerequisite(s): STA 208 or ECS 171; or Consent of Instructor. Emerging problems in molecular biology and current machine learning-based solutions to those problem. How deep learning, kernel methods, graphical models, feature selection, non-parametric models and other techniques can be applied to application areas such as gene editing, gene network inference and analysis, chromatin state inference, cancer genomics and single cell genomics. (Letter.) Effective: 2019 Spring Quarter.
BST 252Advanced Topics in Biostatistics (4) Active
Lecture—3 hour(s); Discussion/Laboratory—1 hour(s). Prerequisite(s): BST 222; BST 223. Biostatistical methods and models selected from the following: genetics, bioinformatics and genomics;longitudinal or functional data; clinical trials and experimental design; analysis of environmental data; dose-response, nutrition and toxicology; survival analysis; observational studies and epidemiology; computer-intensive or Bayesian methods in biostatistics. May be repeated for credit with consent of advisor when topic differs. (Same course as STA 252.) (Letter.) Effective: 2002 Fall Quarter.
BST 290Seminar in Biostatistics (1) Active
Seminar—1 hour(s). Restricted to graduate standing. Seminar on advanced topics in the field of biostatistics. Presented by members of the Biostatistics Graduate Group and other guest speakers. May be repeated up to 12 Time(s). (S/U grading only.) Effective: 2002 Fall Quarter.
BST 298Directed Group Study (1-5) Active
Variable—3-15 hour(s). Special topics in Biostatistics appropriate for group study at the graduate level. May be repeated for credit. (Letter.) Effective: 2004 Spring Quarter.
BST 299Special Study for Biostat Graduate Students (1-12) Active
Variable—3-36 hour(s). Special topics in Biostatistics appropriate for directed individual study on advanced topics not otherwise covered in the Biostatistics curriculum. May be repeated for credit. (S/U grading only.) Effective: 2004 Spring Quarter.
BST 299DDissertation Research (1-12) Active
Variable—3-36 hour(s). Prerequisite(s): and Consent of Instructor. Advancement to Candidacy for Ph.D. Research in Biostatistics under the supervision of major professor. May be repeated for credit. (S/U grading only.) Effective: 2004 Spring Quarter.