Biostatistics (BIOSTAT)

BIOSTAT 200  Biostatistical Methods in Clinical Research I  (3 Units)  

Instructor(s): Ali 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.

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.

View full course details:

  • School: Graduate Division
  • Department: Clinical Research Program
  • May the student choose the instructor for this course? No
  • Does enrollment in this course require instructor approval? No
  • Graduate Division course: Yes
  • Is this a web-based online course? No
  • Is this an Interprofessional Education (IPE) course? No
  • May students in the Graduate Division (i.e. pursuing Master or PhD) enroll in this course? Yes
  • Repeat course for credit? No

BIOSTAT 202  Opportunities and challenges of complex biomedical data  (3 Units)  

Instructor(s): Karla Lindquist

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.

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.

View full course details:

  • School: Graduate Division
  • Department: Clinical Research Program
  • May the student choose the instructor for this course? No
  • Does enrollment in this course require instructor approval? No
  • Graduate Division course: Yes
  • Is this a web-based online course? No
  • Is this an Interprofessional Education (IPE) course? No
  • May students in the Graduate Division (i.e. pursuing Master or PhD) enroll in this course? Yes
  • Repeat course for credit? No

BIOSTAT 208  Biostatistical Methods II  (3 Units)  

Instructor(s): Aaron 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.

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.

View full course details:

  • School: Graduate Division
  • Department: Clinical Research Program
  • May the student choose the instructor for this course? No
  • Does enrollment in this course require instructor approval? No
  • Graduate Division course: Yes
  • Is this a web-based online course? No
  • Is this an Interprofessional Education (IPE) course? No
  • May students in the Graduate Division (i.e. pursuing Master or PhD) enroll in this course? Yes
  • Repeat course for credit? No

BIOSTAT 209  Biostatistical Methods III  (3 Units)  

Instructor(s): Chiung-Yu Huang

Prerequisite(s): EPIDEMIOL 202, BIOSTAT 208. Possession of a graduate or professional doctoral degree (MD, PhD, DDS, PharmD, or international equivalent), currently enrolled in an undergraduate, graduate, or professional school, or relevant work experience. 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.

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.

View full course details:

  • School: Graduate Division
  • Department: Clinical Research Program
  • May the student choose the instructor for this course? No
  • Does enrollment in this course require instructor approval? No
  • Graduate Division course: Yes
  • Is this a web-based online course? No
  • Is this an Interprofessional Education (IPE) course? No
  • May students in the Graduate Division (i.e. pursuing Master or PhD) enroll in this course? Yes
  • Repeat course for credit? No

BIOSTAT 210  Biostatistical Methods IV  (2 Units)  

Instructor(s): Dave 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.

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 covered 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.

View full course details:

  • School: Graduate Division
  • Department: Clinical Research Program
  • May the student choose the instructor for this course? No
  • Does enrollment in this course require instructor approval? No
  • Graduate Division course: Yes
  • Is this a web-based online course? No
  • Is this an Interprofessional Education (IPE) course? No
  • May students in the Graduate Division (i.e. pursuing Master or PhD) enroll in this course? Yes
  • Repeat course for credit? No

BIOSTAT 211  Mathematical Foundations of Biostatistics  (2 Units)  

Instructor(s): Fei 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.

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

View full course details:

  • School: Graduate Division
  • Department: Epidemiology And Translational Sciences Program
  • May the student choose the instructor for this course? No
  • Does enrollment in this course require instructor approval? No
  • Graduate Division course: Yes
  • Is this a web-based online course? No
  • Is this an Interprofessional Education (IPE) course? No
  • May students in the Graduate Division (i.e. pursuing Master or PhD) enroll in this course? Yes
  • Repeat course for credit? No

BIOSTAT 212  Introduction to Statistical Computing in Clinical Research  (1 Units)  

Instructor(s): Aida 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.

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.

View full course details:

  • School: Graduate Division
  • Department: Clinical Research Program
  • May the student choose the instructor for this course? No
  • Does enrollment in this course require instructor approval? No
  • Graduate Division course: Yes
  • Is this a web-based online course? No
  • Is this an Interprofessional Education (IPE) course? No
  • May students in the Graduate Division (i.e. pursuing Master or PhD) enroll in this course? No
  • Repeat course for credit? No

BIOSTAT 213  Programming for Health Data Science in R  (2 Units)  

Instructor(s): Stathis Gennatas

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

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

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.

View full course details:

  • School: Graduate Division
  • Department: Clinical Research Program
  • May the student choose the instructor for this course? No
  • Does enrollment in this course require instructor approval? No
  • Graduate Division course: Yes
  • Is this a web-based online course? No
  • Is this an Interprofessional Education (IPE) course? No
  • May students in the Graduate Division (i.e. pursuing Master or PhD) enroll in this course? Yes
  • Repeat course for credit? No

BIOSTAT 214  Programming for Health Data Science in R II  (2-3 Units)  

Instructor(s): Stathis Gennatas

Prerequisite(s): BIOSTAT 213 or equivalent.

Restrictions: This is a core course of the Health Data Science (HDS) program and part of the Training in Clinical Research Program and may have space limitations. Auditing is not permitted.

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.

View full course details:

  • School: Graduate Division
  • Department: Clinical Research Program
  • May the student choose the instructor for this course? No
  • Does enrollment in this course require instructor approval? No
  • Graduate Division course: Yes
  • Is this a web-based online course? No
  • Is this an Interprofessional Education (IPE) course? No
  • May students in the Graduate Division (i.e. pursuing Master or PhD) enroll in this course? Yes
  • Repeat course for credit? No

BIOSTAT 215  Strengthening causal inferences based on observational data  (3 Units)  

Instructor(s): Thomas Newman

Prerequisite(s): EPIDEMIOL 203, BIOSTAT 208, and BIOSTAT 209 (may be enrolled concurrently). 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.

The course will define causal effects in terms of potential outcomes, show when standard regression methods do and do not support causal inferences, and show how to estimate and interpret marginal and conditional causal effects. It will also cover propensity scores, inverse probability weighting, marginal structural models (for time-dependent treatments with time-dependent confounder/mediators), mediation analysis, new-user designs, instrumental variables, and principal stratification.

View full course details:

  • School: Graduate Division
  • Department: Clinical Research Program
  • May the student choose the instructor for this course? No
  • Does enrollment in this course require instructor approval? No
  • Graduate Division course: Yes
  • Is this a web-based online course? No
  • Is this an Interprofessional Education (IPE) course? No
  • May students in the Graduate Division (i.e. pursuing Master or PhD) enroll in this course? Yes
  • Repeat course for credit? No

BIOSTAT 216  Machine Learning in R for the Biomedical Sciences  (3 Units)  

Instructor(s): Adam Olshen

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.

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.\r\n

View full course details:

  • School: Graduate Division
  • Department: Clinical Research Program
  • May the student choose the instructor for this course? No
  • Does enrollment in this course require instructor approval? No
  • Graduate Division course: Yes
  • Is this a web-based online course? No
  • Is this an Interprofessional Education (IPE) course? No
  • May students in the Graduate Division (i.e. pursuing Master or PhD) enroll in this course? Yes
  • Repeat course for credit? No

BIOSTAT 272  Foundations in Biostatistical Principles and Methods  (4 Units)  

Instructor(s): Patrick 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

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.

View full course details:

  • School: Graduate Division
  • Department: Pharmaceutical Science And Pharmacogenomics Prog
  • May the student choose the instructor for this course? No
  • Does enrollment in this course require instructor approval? No
  • Graduate Division course: Yes
  • Is this a web-based online course? No
  • Is this an Interprofessional Education (IPE) course? No
  • May students in the Graduate Division (i.e. pursuing Master or PhD) enroll in this course? Yes
  • Repeat course for credit? No

BIOSTAT 273  Introduction to Biostatistics  (1 Units)  

Instructor(s): David Quigley

Prerequisite(s): None

Restrictions: None

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.

View full course details:

  • School: Graduate Division
  • Department: Pharmaceutical Science And Pharmacogenomics Prog
  • May the student choose the instructor for this course? No
  • Does enrollment in this course require instructor approval? No
  • Graduate Division course: Yes
  • Is this a web-based online course? Yes
  • Is this an Interprofessional Education (IPE) course? No
  • May students in the Graduate Division (i.e. pursuing Master or PhD) enroll in this course? Yes
  • Repeat course for credit? No