Course Catalog » Course Listing for Biostatistics

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

Instructor(s): A. Mirzazadeh       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: Direct - Lecture, Direct - Lab-Skills, Student - Lecture, Student - Lab-Skills, Student - Project

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): A. Scheffler       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: Direct - Lecture, Direct - Lab-Skills, Direct - Project, Student - Lecture, Student - Lab-Skills

This is an introduction to the opportunities and challenges of using large datasets for biomedical research. Topics to be covered include: What makes big data different? 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. Introduction to supervised and unsupervised machine learning including detailed discussion of algorithms and model fitting.

208  Biostatistical Methods II  (3 units)   Winter

Instructor(s): A. Scheffler       Prerequisite(s): Designing Clinical Research (EPI 202), and Biostatistical Methods I (BIOSTAT 200). 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.       Activities: Direct - Lecture, Student - 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 202 and BIOSTAT 208.

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

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 and Epidemiology 202 and Biostatistics 208 and 209. 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.       Activities: Direct - Lecture, Student - 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.

211  Mathematical Foundations of Biostatistics  (2 units)   Winter

Instructor(s): F. Jiang       Prerequisite(s): Calculus is a prerequisite for this class. For example, students must understand integration and derivatives. A previous or concurrent course in introductory biostatistics is preferred, BIOSTAT 200

Restrictions: This course is part of the Epidemiology and Translational Science PhD program and may have space limitations. Auditing is not permitted.       Activities: Direct - Lecture, Student - Lecture

The goal of this course is to equip students with core statistical concepts and methods. In this course students will learn mathematical, computational, statistical and probabilistic background; the basics of probability distributions including the definitions of density functions, cumulative distributions, moments of the distributions; theory and methods for point estimation; and methodology for the construction of hypothesis testing and confidence intervals. R statistical software will be used

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

Instructor(s): A. Venado Estrada       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: Direct - Lecture, Student - Lecture

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 Programming for Health Data Science in R  (1.5 units)   Summer

Instructor(s): E. Gennatas       Prerequisite(s): No prior programming experience is required.

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

Vast amounts of health-related data are being generated daily and at an increasing rate. Our ability to extract insights and make the most of these resources depends on the effective and efficient use of computational tools to preprocess, visualize, and analyze different types of data. BIOSTAT 213 is an introductory programming course which aims to provide hands-on experience in the R language and enable further work in biostatistics, epidemiology, and machine learning/health data science.

214  Programming for Health Data Science in R II  (1.5 units)   Fall

Instructor(s): E. Gennatas, J. Kornak       Prerequisite(s): BIOSTAT 213 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: Direct - Lecture, Direct - Lab-Skills, Student - Lecture

R programming course to enable work in any field including biostatistics, epidemiology, data science/machine learning. This course builds on students’ prerequisite core R language knowledge to cover skills in advanced data transformations, visualization, working with big (in-memory) data, report-writing, and core statistic testing.

215  Strengthening causal inferences based on observational data  (3 - 4 units)   Spring

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

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

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.

216  Machine Learning in R for the Biomedical Sciences  (3 units)   Winter

Instructor(s): J. Feng       Prerequisite(s): BIOSTAT 208, BIOSTAT 213 & BIOSTAT 209. Exceptions to these prerequisites may be made with the consent of the Course Director, space permitting. Strongly recommended: EPI 204 & BIOSTAT 202

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

This is a course that covers machine learning methods as they apply to areas of biomedical research and will teach how to implement the methods in R. Topics to be covered include: What is Machine learning? Prediction techniques (including classification) and methods for assessing them, Cross-validation, penalized regression methods such as lasso, boosting, bagging and ensemble methods, pattern recognition, deep learning, and data reduction methods, and machine learning meta packages in R.

272  Foundations in Biostatistical Principles and Methods  (4 units)   Fall

Instructor(s): P. Phillips       Prerequisite(s): There are no formal prerequisites. Students are expected to have knowledge of undergraduate statistics. We will primarily use the R programming language, so familiarity with R is helpful. Students are encouraged to take advantage of the PSPG R programming bootcamp

Restrictions: None       Activities: Direct - Lecture, Direct - Workshop, Student - Lecture, Student - Workshop, Student - Project, Student - Independent Study

This course provides a foundation in modern biostatistical methods and statistical reasoning for pharmaceutical sciences research. The course will explore common data types and distributions, experimental design, exploratory data analysis, methods for hypothesis testing (both parametric and non-parametric), and model-building and comparison. During this hands-on course, students will reinforce their understanding by implementing what they have learned in R.

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.