Course Catalog » Course Listing for Biostatistics

187  Intro Stat Theory & Practice  (5 units)   Fall

Instructor(s): S. Paul       Prerequisite(s): Previous course in introductory statistics.

Activities: Lecture, Laboratory

Review of basic statistical theory, sampling, descriptive statistics, and probability. Presentation of confidence intervals, hypothesis testing, one- and two-factor analysis of variance, correlation, simple linear regression, and chi-square tests. A preparation for more advanced work.

192  Introduction to Linear Models  (5 units)   Winter

Instructor(s): S. Paul       Prerequisite(s): Biostatistics 183 or 187 or equivalent or permission of instructor

Restrictions: None       Activities: Lecture, Laboratory

This course begins with bivariate correlation and simple linear regression and then moves on to a presentation of multiple regression techniques and the analysis of variance under the general model. The focus is on the choice of technique and interpretation of results rather than on mathematical development of the methods.

200  Biostatistical Methods in Clinical Research I  (3 units)   Fall

Instructor(s): I. Allen       Prerequisite(s): EPI 202, BIOSTAT 212 and possession of a MD, PhD, DDS or PharmD or equivalent doctoral degree. Exceptions to these prerequisites may be made with the consent of the Course Director, space permitting.

Restrictions: This course is part of the Training in Clinical Research (TICR) Program and may have space limitations. Auditing is not permitted.      

Course is an introduction to the study of biostatistics. Course addresses types of data, their summarization, exploration and explanation, as well as concepts of probability and their role in explaining uncertainty. Course concludes with coverage of inference applied to means , proportions, regression coefficients and contingency tables. Throughout the course, the software program STATA will be used.

202  Opportunities and challenges of complex biomedical data  (3 units)   Summer

Instructor(s): D. Glidden       Prerequisite(s): None

Restrictions: This course is part of the Training in Clinical Research (TICR) Program and may have space limitations. Auditing is not permitted.       Activities: Lecture, Laboratory, Project

This is an introduction to the opportunities and challenges of using large datasets for biomedical research. Topics to be covered include: What is data science/big data? What makes it different from non-big data? What big data can and cannot do. Phases of data science: getting data, merging and cleaning data, storing and accessing data, visualizing or telling stories with data, drawing conclusions from data.

208  Biostatistical Methods II  (3 units)   Winter

Instructor(s): S. Shiboski       Prerequisite(s): Possession of MD, PhD, DDS or PharmD degree or permission of course director and Epidemiology 180.04 and Biostat 183 or equivalent.

Restrictions: This course is part of the Training in Clinical Research (TICR) Program and may have space limitations. Auditing is not permitted.       Activities: Lecture

Instruction in multiple predictor analyses as a tool for control of confounding and for constructing predictive models. Topics will include exploratory data analyses, linear regression, and logistic regression. The STATA statistical package will be used.

209  Biostatistical Methods III  (3 units)   Spring

Instructor(s): C. Huang       Prerequisite(s): Possession of MD, PhD, DDS or PharmD degree or permission of course director and Epidemiology 180.04 and Biostat 183 and 208.

Restrictions: This course is part of the curriculum for the Advanced Training in Clinical Research (ATCR) Certificate Program and the Master's Degree Program in Clinical Research. This course has special fees attached for non-matriculated students.       Activities: Lecture, Laboratory

Advanced instruction in multiple predictor analyses. Topics will include survival analysis and regression for repeated measures. In the final weeks of the course, participants will receive individualized instruction for the analysis of their own data.

210  Biostatistical Methods IV  (2 units)   Fall

Instructor(s): D. Glidden       Prerequisite(s): Possession of MD, PhD, DDS or PharmD degree or permission of course director and Epidemiology 202 and Biostatistics 208 and 209.

Restrictions: This course is part of the Training in Clinical Research (TICR) Program and may have space limitations. Auditing is not permitted.       Activities: Lecture

This is a continuation of the Biostatistical Methods in Clinical Research series, covering additional methods in multi-predictor analyses and allowing more in-depth exploration of the topics cobered in Biostat I, II and III. Topics in survival analysis and longitudinal analysis will be emphasized and students are also encouraged to utilize their own projects to motivate discussion and to suggest topics of interest.

212  Introduction to Statistical Computing in Clinical Research  (1 units)   Summer

Instructor(s): J. Martin       Prerequisite(s): EPI 180.04 and possession of a MD, PhD, DDS or PharmD or equivalent doctoral degree. Exceptions to these prerequisites may be made with the consent of the Course Director, space permitting.

Restrictions: This course is part of the Training in Clinical Research (TICR) Program and may have space limitations. Auditing is not permitted. Preference is given to UCSF-affiliated personnel.        Activities: Lecture, Laboratory

This course will introduce clinical researchers to the use of computer software for managing and analyzing clinical research data. Currently available statistical packages will be described and the roles of spreadsheet and relational database programs discussed. Use of STATA for managing, cleaning, describing, and analyzing data will be taught in lecture and laboratory sessions.

213  Introduction to Computing in R  (1 units)   Winter

Instructor(s): J. Martin       Prerequisite(s): Basic computer skills. Possession of an undergraduate degree.

Restrictions: This course is part of the Training in Clinical Research (TICR) Program and may have space limitations. Auditing is not permitted.       Activities: Lecture

In the current era of burgeoning data availability, many computational and analytic techniques can only be performed with certain software packages. In particular, many techniques in predictive analytics and cluster analysis can only be implemented using a small number of software programs. The R program is one such program. This course will provide an introduction to the use of R and serve as a critical foundation for subsequent methodologic courses that require R for computation.

215  Strengthening causal inferences based on observational data  (3 units)   Spring

Instructor(s): T. Newman       Prerequisite(s): EPIDEMIOL 203 BIOSTAT 208 BIOSTAT 209

Restrictions: Should have a working understanding of the following: confounding, mediation, and interaction; directed acyclic graphs; linear, logistic, Cox, and repeated measures regression models; how to implement and check these models in Stata; and basic data management skills.       Activities: Lecture, Laboratory, Project

The course will define average causal effects in terms of potential outcomes, show when standard regression methods support causal inferences, and show how to estimate and interpret marginal causal effects. It will also cover propensity scores, for rare outcomes but common binary exposures; marginal structural models, for time-dependent treatments with time-dependent confounder/mediators; new-user designs; instrumental variables, for data with important unmeasured confounders.

226  Biostatistical Methods for Clinical Research V  (1 units)   Winter

Instructor(s): J. Hilton       Prerequisite(s): Epidemiology 202, Biostat 200, 208, 209. Exceptions may be made with the consent of the course director, space permitting.

Restrictions: This course is part of the Training in Clinical Research (TICR) Program and may have space limitations. Auditing is not permitted.       Activities: Lecture

Instruction in advanced topics in biostatistics: current issues in the design and analysis of randomized clinical trials.

250  Research  (1 - 8 units)   Fall, Winter, Spring

Instructor(s): Staff

273  Introduction to Biostatistics  (1 units)   Fall

Instructor(s): D. Quigley       Prerequisite(s): None

Restrictions: None       Activities: Web-based course work, Workshop

This course provides an introduction to biostatistical methods. The course emphasizes practical considerations required to design studies, perform elementary analysis, and become an informed consumer of statistical data. Topics include study design, exploratory data analysis, the P value and hypothesis testing, power analysis, and reproducible analysis methods using the R statistical environment.