Summer 2025 Schedule

The application for Summer 2025 will be available May 16.

Designing Clinical Research (EPI 202)
These courses provide instruction in developing a clinical or epidemiologic research question and creating a concise protocol that includes literature review, study design, participant sampling and recruitment, instruments and other measurement approaches, sample size, consent form, budget and timetable. Each trainee reviews and supports the work of colleagues. The course closely follows the textbook Designing Clinical Research, by W. Browner and other TICR Program faculty, now in its fifth edition. EPI 202 is intended for doctoral students, fellows, or faculty members.

Data Collection and Management Systems for Clinical Research (EPI 218)
Instruction in choosing the appropriate data management system, design of research databases, options in data entry, form and report generation, computer security, and budgeting for data management personnel and equipment.

Introduction to Clinical Informatics (EPI 232)
This course will provide an overview of clinical informatics (the application of informatics to deliver health care services), with an emphasis on clinical informatics research and maintaining scientific rigor in implementation, measurement, evaluation, and health equity.

Introduction to Statistical Computing in Clinical Research (BIOSTAT 212)
Instruction in the use of statistical software for exploring and analyzing clinical research data. While the roles of spreadsheet and relational database programs will be discussed, the course will focus on the STATA statistical software package for analyzing and presenting data.

Opportunities and Challenges of Complex Biomedical Data: Introduction to the Science of "Big Data" (DATASCI 202)
Introduction to the opportunities and challenges of using biological and health-related "big data" to perform biomedical research.

Programming for Health Data Science in R (DATASCI 213)
Instruction in how to use R computer software, including how to import data files; assign and manipulate objects; prepare data for basic statistical analysis; generate graphs and tables; write functions for iterative application; generate reports using R Markdown; and become prepared to take advanced courses in data analysis which require computation and programming in R.

​​Designing Clinical Research for Clinical Residents (EPI 150.03)
This course provides instruction in developing a clinical research question and creating a concise protocol that includes literature review, study design, subject sampling and recruitment, instruments and other measurement approaches, sample size, consent form, budget and timetable. Each trainee reviews and supports the work of colleagues. The course closely follows the textbook by W. Browner and other TICR faculty, now in its fifth edition. This course is intended for clinical residents at UCSF.

Epidemiologic Methods (EPI 203)
Instruction in the diverse array of study designs and their theoretical interrelatedness, available in clinical and epidemiologic research; the importance of measurement; different types of measures of disease occurrence; methods to measure exposure - disease association; measures of attributable risk; effect-measure modification; approaches to identify and minimize selection, measurement and confounding bias; and conceptual motivation for more sophisticated methods (e.g., regression or marginal structural approaches) to manage confounding, which are increasingly common tools in epidemiologic analyses.

Clinical Epidemiology (EPI 204)
Instruction in developing and interpreting metrics commonly used in evidence-based clinical medicine (e.g., screening tests, diagnostic tests, and prognostic tests), as well as any field that seeks to make predictions of concurrent or future conditions or events.

Epidemiology of Aging (EPI 210)
Instruction in the issues and methods for studying the epidemiology of aging, focusing on common chronic diseases in older populations.

TICR Program Seminar for First-Year Master’s and Certificate Program Scholars (EPI 220/230)
Biweekly seminar for first-year students to present their research to their colleagues and faculty.

Masters Seminar II Fall (EPI 221)
Biweekly seminar for second-year students to present their projects and specialized methodologic topics to their colleagues and faculty.

Demographic Methods for Health (EPI 263)
This seminar course offers an introduction to the methods involved in epidemiologic research of aging and to age-related conditions. Students will learn to integrate epidemiologic methods and subject matter knowledge to critically appraise literature about aging and age-related conditions, including the identification of common biases (e.g. selection bias) and best practices to ameliorate these biases. This course will therefore introduce and/or reinforce foundational principles of modern epidemiology via the study of substantive topics relevant to the epidemiology of aging.

Biostatistical Methods for Clinical Research I (BIOSTAT 200)
Introduction to descriptive statistics, distributions, probability, exploratory data analysis, and selected variable parametric and non-parametric inference. Statistical software will be used throughout to implement concepts learned in class and to allow scholars to begin to explore their own data.

Biostatistical Methods for Clinical Research IV (BIOSTAT 210)
A continuation in the biostatistics for clinical research sequence, covering advanced methods for building and evaluating regression models. The emphasis is on methods that cut across common families of regression models in biostatistics: predictor selection, model diagnostics, and missing data. The statistics package STATA will be used throughout the course.

Programming for Health Data Sciences In R II (BIOSTAT 214)
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.

Introduction to Python and Data Science Tools (DATASCI 217)
Introductory course on the essential tools and skills for data science, focusing on Python programming and industry-relevant tools.

Bayesian Methods and Gaussian Processes (DATASCI 226)
An introductory course on Bayesian statistics, Markov Chain Monte Carlo (MCMC) sampling, and Gaussian Processes.

Implementation Science courses
DEB students in the PhD, MS, or Certificate programs, who are interested in Fall implementation science courses, please contact your program administrator.  All other learners interested in IMS 245, IMS 248, IMS 243, please visit the Implementation Science Program

Grant Writing Workshop on Mentored Career Development Awards
Instruction in writing successful grant applications for NIH-mentored career development awards. The workshop uses examples from patient-oriented research career development awards (K23s). Underlying concepts for the career development plan, mentoring plan, and research plan also apply to research scientist development awards (K01s) and clinical scientist development awards (K08s).

Winter Schedule

Clinical Trials (EPI 205)
Instruction in experimental study design options; methods of randomization; blinding, interventions and controls; measuring outcomes and adverse effects; follow-up, compliance and postrandomization problems; ethical issues; and working with pharmaceutical companies.

Epidemiologic Methods II (EPI 207)
Instruction in the interrelationships between various measures of disease occurrence and association; concepts of attributable risk; interactions; practical and theoretical considerations of the most common study designs in observational research; methods of reducing confounding including matching, instrumental variables, and propensity scores.

Cost-Effectiveness Analysis in Medicine and Public Health (EPI 213)
Instruction in creating decision trees; obtaining input values for probabilities, utilities, and costs; and calculating health, cost and cost-effectiveness outcomes. Course participants will design and complete group-based decision and cost effectiveness analyses using customized software.

Molecular and Genetic Epidemiology I (EPI 217)
Will not be taught during 2024-2025 academic year.
Introduction to the concepts, principles, and use of molecular and genetic methods in epidemiologic and clinical research and how to develop a framework for interpreting, assessing, and incorporating molecular and genetic measures in research.

TICR Program Seminar for First-Year Master’s and Certificate Program Scholars (EPI 220/230)
Biweekly seminar for first-year students to present their research to their colleagues and faculty.

Masters Seminar II Winter (EPI 221)
Biweekly seminar for second-year students to present their projects and specialized methodologic topics to their colleagues and faculty.

Social Determinants of Health and Health Disparities: What Every Researcher Should Know (EPI 222)
An introduction to the knowledge and skills needed to conduct high-quality research in diverse human populations with an emphasis on understanding the measurement and influence of race/ethnicity and socioeconomic status on health. The 1-unit, 5-week option is relevant for any researcher who intends to work with human subjects, and the 2-unit, 10-week option will cover advanced material for students with a more focused interest in race and socioeconomic-based health disparities.

Informatics Tools for Health Disparities Research (EPI 226)
This course is designed for learners interested in accessing data sources and using informatics tools that are helpful in identifying cohorts, developing research questions, and conducting health disparities research.

Use of Electronic Health Record Data for Research (EPI 231)
Introduction to electronic health record (EHR) data, including relational database and data warehouse models as they pertain to EHRs; medical vocabularies and ontologies used in EHRs; construction of patient cohorts based on structured data, such as diagnosis codes, encounters, and procedures; extraction of relevant associated data for a specified patient cohort into analytic files; and formulating research questions that benefit from the strengths and limit weaknesses of EHR data.

Cancer Epidemiology (EPI 252)
Will not be taught during 2024-2025 academic year.
Instruction in how the principles and methods of epidemiology can be applied to the study of neoplastic diseases.

NIH F & K Grant Writing Workshop (Online) (EPI 258 A) <-- Class full
​​​This course is designed to clarify early investigators’ research and career goals and to learn the different components of NIH pre- and post-doctoral grants.

Equity Issues in Reproductive Health (EPI 269)
Will not be taught during 2024-2025 academic year.
This is a graduate-level course focused on Reproductive, Maternal, Neonatal and Child Health (RMNCH). The course will cover foundational RMNCH concepts, including providing an overview of selected RMNCH issues in the US and globally, highlighting best practices and innovations in measurement in RMNCH, examining ways in which social determinants influence RMNCH and produce health inequities and evaluating approaches to meet the needs of vulnerable populations.

Biostatistical Methods for Clinical Research II (BIOSTAT 208)
Instruction in multiple predictor analyses as a tool for control of confounding and for constructing predictive models. Topics will include linear regression and logistic regression. The Stata statistical package will be used throughout.

Mathematical Foundations of Biostatistics (BIOSTAT 211)
Will not be taught during 2024-2025 academic year.
This course was designed to equip students with core statistical concepts and methods. Topics include 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.

Machine Learning in R for the Biomedical Sciences: Methods for Prediction, Pattern Recognition, and Data Reduction (BIOSTAT 216)
An introduction to use of machine learning (automated statistical algorithms applied to complex data structures) to solve problems of prediction, pattern recognition, and data reduction in various fields related to biomedical research; the R software environment will be used throughout.

Implementation Science courses
DEB students in the PhD, MS, or Certificate programs, who are interested in winter implementation science courses, please contact your program administrator.  All other learners interested in IMS 242, IMS 246, IMS 267, please visit the Implementation Science Program

Grant Writing Workshop on Mentored Career Development Awards
Instruction in writing successful grant applications for NIH mentored career development awards. Workshop uses examples from patient-oriented research career development awards (K23s). Underlying concepts for the career development plan, mentoring plan, and research plan also apply to research scientist development awards (K01s) and clinical scientist development awards (K08s).

*enrollment in this course (or track) not guaranteed due to high demand

Spring 2025 Course Schedule

Publishing and Presenting Clinical Research (EPI 212)
Instruction in preparing abstracts for scientific conferences; the essential components of a research manuscript (Introduction, Methods, Results, Discussion); and navigating the submission and revision aspects of the peer-review process.

TICR Program Seminar for First-Year Master’s and Certificate Program Scholars (EPI 220/230)
Biweekly seminar for first-year students to present their research to their colleagues and faculty.

Masters Seminar II Spring (EPI 221)
Biweekly seminar for second-year students to present their projects and specialized methodologic topics to their colleagues and faculty.

Introduction to Clinical Artificial Intelligence (EPI 233)
The course will provide an overview of artificial intelligence (AI), with a particular emphasis on clinical informatics applications and considerations. Topics covered will include research, maintaining scientific rigor in clinical informatics AI design decisions, evaluation, interpretability, privacy, and fairness/bias.

Implementation Science courses
DEB students in the PhD, MS, or Certificate programs, who are interested in spring implementation science courses, please contact your program administrator.  All other learners interested in IMS 241, IMS 247, IMS 249, please visit the Implementation Science Program

NIH F & K Grant Writing Workshop (EPI 258 B)
Will not be taught during 2024-2025 academic year.
This course is designed to clarify early investigators’ research and career goals and to learn the different components of NIH pre- and post-doctoral grants.

Epidemiologic Methods III (EPI 265)
This course will focus on advanced analytic methods for causal inference and the interpretation of epidemiologic studies for decision making for individual and population health. The course will emphasize causal inference from observational data, and will include a focus on data generation processes, specific methodological concepts for epidemiologic studies, and interpretation of data.  Please note attendance is required for this course.

Mathematical Modeling of Infectious Diseases (EPI 266)
Introduction to concepts of mathematical modeling of infectious diseases; topics include branching processes and the basic reproduction number, dynamical systems, and methods for data fitting including Markov chain Monte Carlo.

Econometric Methods for Causal Inference (EPI 268)
Will not be taught during 2024-2025 academic year.
Instruction on estimating the causal effects of health-related policies, programs, and interventions using observational data and methods developed in the field of econometrics. Topics include difference-in-differences, instrumental variables, and regression discontinuity.

Biostatistical Methods for Clinical Research III (BIOSTAT 209)
A continuation of the Winter Quarter course in multivariable statistical analysis that includes instruction in survival analysis and analysis of repeated measures and clustered data. The course culminates with student presentations of statistical analyses of their own research projects.

Advanced Approaches to the Analysis of Observational Data (BIOSTAT 215)
A common goal of observational clinical or epidemiologic research is to estimate the causal effect of particular exposures or interventions on some health outcome. This course will describe more advanced methods that may succeed in estimating causal effects in cases where standard approaches break down..

Applied Data Science with Python (DATASCI 223)
Survey of Data Science methods in Python, starting with common data science tools and processes and spending one week per topics learning to build common ML/AI solutions.

Understanding Machine Learning: From Theory to Applications (DATASCI 224)
This course teaches the mathematical foundations of machine learning (ML). Each week, the course surveys a different algorithm to examine its underlying machinery, covering topics such as linear algebra, calculus, and optimization. ML algorithms range from linear models to gradient boosting and deep learning. The course also discusses newer concepts such as model fairness and ML for causal inference. Upon course completion, students should be able to learn new ML algorithms independently.

Grant Writing Workshop on Mentored Career Development Awards
Instruction in writing successful grant applications for NIH-mentored career development awards. The workshop uses examples from patient-oriented research career development awards (K23s). Underlying concepts for the career development plan, mentoring plan, and research plan also apply to research scientist development awards (K01s) and clinical scientist development awards (K08s).