The course program offered at UC Davis by the Biostatistics Graduate Group allows for comprehensive training in all areas of Biostatistics. The coursework includes, among others, Mathematical Statistics (STA 231ABC), Linear Models (STA 232AB), Statistical Computing (STA 243), Applied Multivariate Analysis (STA 135), Analysis of Categorical Data (STA 138) and the following core courses:
BST 222: Survival Analysis (4 units)
Lecture-3 hours; discussion/laboratory-1 hour. Prerequisite: Statistics 131C. Incomplete data; life tables; nonparametric methods; parametric methods; accelerated failure time models; proportional hazards models; partial likelihood; advanced topics. (Same course as Statistics 222.)-I.
BST223: Generalized Linear Models (4 units)
Lecture-3 hours; discussion/laboratory-1 hour. Prerequisite: Statistics 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 Statistics 223.)-II.
BST 224: Analysis of Longitudinal Data (4 units)
Lecture-3 hours; discussion/laboratory-1 hour. Prerequisite: course/Statistics 222, 223, Statistics 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 Statistics 224.)-III. (III.)
BST 225: Clinical Trials (4 units)
Lecture-3 hours; discussion/laboratory-1 hour. Prerequisite: course/Statistics 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 Statistics 225.) Offered in alternate years.-III.
BST 226: Statistical Methods for Bioinformatics (4 units)
Lecture-3 hours; discussion/laboratory-1 hour. Prerequisite: course 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 Statistics 226.) Offered in alternate years.-II.
BST 227—Machine Learning in Genomics (4 units)
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. Effective: 2019 Spring Quarter.
BST 252: Advanced Topics in Biostatistics (4 units)
Lecture-3 hours; discussion/laboratory-1 hour. Prerequisite: course 222, 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 adviser when topic differs. (Same course as Statistics 252.) Offered in alternate years.-III.
BST 290: Seminar in Biostatistics (1 unit)
Seminar-1 hour. Seminar on advanced topics in the field of biostatistics. Presented by members of the Biostatistics Graduate Group and other guest speakers. May be repeated for up to 12 units of credit. (S/U grading only.)- II, III.
BST 298: Directed Group Study (1-5 units)
Prerequisite: consent of instructor.
BST 299: Special Study for Biostatistics Graduate Students (1-12 units)
Prerequisite: consent of instructor. (S/U grading only.)
BST 299D: Dissertation Research (1-12)
Prerequisite: advancement to Candidacy for Ph.D. and consent of instructor. Research in biostatistics under the supervision of major professor. (S/U grading only.)