Biostatistics (BIOSTAT)

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

Offered In: Fall
  

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.

Activities: Lecture, Project, Lab skills

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.

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  • School: Graduate Division
  • Department: Clinical and Epidemiological Research Program
  • May the student choose the instructor for this course? No
  • Does enrollment in this course require instructor approval? No
  • Course Grading Convention: Letter Grade, P/NP (Pass/Not Pass) or S/U (Satisfactory/Unsatisfactory)
  • 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)  

Offered In: Winter
  

Instructor(s): Aaron Scheffler

Prerequisite(s): EPIDEMIOL 202, 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: 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.

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  • School: Graduate Division
  • Department: Clinical and Epidemiological Research Program
  • May the student choose the instructor for this course? No
  • Does enrollment in this course require instructor approval? No
  • Course Grading Convention: Letter Grade, P/NP (Pass/Not Pass) or S/U (Satisfactory/Unsatisfactory)
  • 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)  

Offered In: Spring
  

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.

Activities: Lecture, Project, Lab science

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.

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  • School: Graduate Division
  • Department: Clinical and Epidemiological Research Program
  • May the student choose the instructor for this course? No
  • Does enrollment in this course require instructor approval? No
  • Course Grading Convention: Letter Grade, P/NP (Pass/Not Pass) or S/U (Satisfactory/Unsatisfactory)
  • 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)  

Offered In: Fall
  

Instructor(s): Dave Glidden

Prerequisite(s): Possession of MD, PhD, DDS, or PharmD degree, and EPIDEMIOL 202 and BIOSTAT 208 and BIOSTAT 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: 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 covered in Biostat I (BIOSTAT 183), II (BIOSTAT 208) and III (BIOSTAT 209). 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.

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  • School: Graduate Division
  • Department: Clinical and Epidemiological Research Program
  • May the student choose the instructor for this course? No
  • Does enrollment in this course require instructor approval? No
  • Course Grading Convention: Letter Grade, P/NP (Pass/Not Pass) or S/U (Satisfactory/Unsatisfactory)
  • 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)  

Offered In: Winter
  

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.

Activities: 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

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  • 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
  • Course Grading Convention: P/NP (Pass/Not Pass) or S/U (Satisfactory/Unsatisfactory)
  • 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)  

Offered In: Summer
  

Instructor(s): Aida Venado Estrada

Prerequisite(s): EPIDEMIOL 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

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.

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  • School: Graduate Division
  • Department: Clinical and Epidemiological Research Program
  • May the student choose the instructor for this course? No
  • Does enrollment in this course require instructor approval? No
  • Course Grading Convention: Letter Grade, P/NP (Pass/Not Pass) or S/U (Satisfactory/Unsatisfactory)
  • 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 215  Strengthening causal inferences based on observational data  (3 Units)  

Offered In: Spring
  

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.

Activities: Lecture, Lab skills, Discussion

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.

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  • School: Graduate Division
  • Department: Clinical and Epidemiological Research Program
  • May the student choose the instructor for this course? No
  • Does enrollment in this course require instructor approval? No
  • Course Grading Convention: Letter Grade, P/NP (Pass/Not Pass) or S/U (Satisfactory/Unsatisfactory)
  • 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)  

Offered In: Fall
  

Instructor(s): Patrick Phillips, Suzanne Dufault

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: Lecture, Project, Workshop

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.

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  • 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
  • Course Grading Convention: P/NP (Pass/Not Pass) or S/U (Satisfactory/Unsatisfactory)
  • 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  (0.5 Units)  

Offered In: Fall
  

Instructor(s): David Quigley

Prerequisite(s): None

Restrictions: None

Activities: 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. This course will emphasize applications in favor of mathematical detail.

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
  • Course Grading Convention: P/NP (Pass/Not Pass) or S/U (Satisfactory/Unsatisfactory)
  • 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