Epidemiology Course Schedule
 

• Spring 2025
• Fall 2024
• Spring 2024
• Fall 2023
• Spring 2023
• Fall 2022
• Spring 2022
• Fall 2021
• Spring 2021
• Fall 2020
• Spring 2020
• Spring 2019
• Fall 2018
• Spring 2018
• Fall 2017

Epidemiology Course Schedule: Spring 2025

• First day of classes: January 15 (Monday Classes)
• MLK, Jr. Day (no classes): January 20
• Course Selection Period Ends: January 28
• Spring Break: March 8-16 
• Last day of classes: April 30
• Reading Days: May 1-4
• Final Examinations: May 5-13
• Spring Term Ends: May 13
• Commencement: May 19

MSCE Electives
Registration Form (PDF)
Independent Study Form (DOC)

Epidemiology Course Schedule: Fall 2024

• First day of classes: Tuesday, August 27 
• Labor Day (no classes): September 2
• Fall Term Break: October 3-6 (observance up to GGEB instructors)
• Thursday-Friday class schedule on Tuesday-Wednesday: November 26-27
• Thanksgiving Break: November 28- December 1 
• Last day of classes: December 9
• Reading Days: December 10-11 
• Final Examinations: December 12-19
• Fall Term Ends: December 19

Registration Form (PDF)
Independent Study Form (DOC)

Epidemiology Course Schedule: Spring 2024

• First day of classes: January 18
• Course Selection Period Ends: January 31
• Course drop period (for graduate students) ends: February 27
• Spring Break: March 2-10
• Last day of classes: May 1
• Reading Days: May 2-5 
• Final Examinations: May 6-14
• Spring Term Ends: May 14
• Commencement: May 20

MSCE Electives
Registration Form (PDF)
Independent Study Form (DOC)

Epidemiology Course Schedule: Fall 2023

• First day of classes: August 29 
• Labor Day (no classes): September 4th
• Fall Term Break: October 12-15 (observance up to GGEB instructors)
• Thursday-Friday class schedule on Tuesday-Wednesday: November 21-22
• Thanksgiving Break: November 23-26 
• Last day of classes: December 11
• Reading Days: December 12-13 
• Final Examinations: December 14-21
• Fall Term Ends: December 21

MSCE Electives

Registration Form (PDF)

Independent Study Form (DOC)

Epidemiology Course Schedule: Spring 2023

• First day of classes: January 11 (Monday classes meet Wednesday)
• MLK, Jr. Day: January 16 (no class)
• Course Selection Period Ends: January 24
• Course drop period (for graduate students) ends: February 20
• Spring Break: March 4-12 
• Last day of classes: April 26
• Reading Days: April 27-30 
• Final Examinations: May 1-9
• Spring Term Ends: May 9
• Commencement: May 15

MSCE Electives

Registration Form (PDF)

Independent Study Form (DOC)

• Epidemiology Course Schedule: Fall 2022

• First day of classes: August 30 (check dates for each class)
• Labor Day: September 5 (no class)
• Course drop period (for graduate students) ends: TBD
• Fall Term Break: October 14-17 (GGEB does not observe)
• Thursday/Friday Class Scheduled on Tuesday/Wednesday: November 22 and 23
• Thanksgiving Break: November 24-27
• Last day of classes: December 12
• Reading Days: December 13-14 
• Final Examinations: December 15-22
• Fall Term Ends: December 22

Epidemiology Course Schedule: Spring 2022

• First day of classes: January 12 (Monday schedule)
•  MLK Jr. Day: January 17th
• Course drop period (for graduate students) ends: February 21 
• Spring Break: March 5 - 13
• Last day of classes: April 28
• Reading Days: April 29-May 1 
• Final Examinations: May 2-10
• Spring Term Ends: May 10
• Commencement: May 16
   

  EPID 702: Advanced Topics in Epidemiological Research	Harhay, Michael and Leonard, Charlie	M	12pm-3pm	1/12-4/25	Blockley 840
  Career Development Workshop Series  (Open to Epi PhD studens only - First Year Students)	Tuton, Lucy and Bogner, Hilary	M	3:15pm-4:30	1/24, 2/21, 3/21,4/18	TBD
  Career Development Workshop Series  (Open to Epi PhD students only - Second Year Students)	Tuton, Lucy and Bogner, Hilary	M	1:30-3pm	1/24, 2/21, 3/21,4/18	TBD

Course Descriptions

Registration Form (PDF)

Independent Study Form (DOC)

Course Descriptions

Registration Form (PDF)

Independent Study Form (DOC)

Epidemiology Course Schedule: Fall 2021

•  First day of classes: August 31 (check dates for each class)
• Labor Day: September 6
• Course drop period (for graduate students) ends: TBD
• Fall Term Break: October 14-17
• Thursday/Friday Class Scheduled on Tuesday/Wednesday: November 23 and 24
• Last day of classes: December 10
• Reading Days: December 11-14 
• Final Examinations: December 15-22
• Fall Term Ends: December 22

Course Descriptions

Registration Form (PDF)

Independent Study Form (DOC)

Epidemiology Course Schedule: Spring 2021

• First day of classes: January 20 (check dates for each class)
• Engagement Day: Friday, February 12th
• Course drop period (for graduate students) ends: February 22 
• Spring Break: March 10 & 11
• Engagement Days: March 30th and April 12th
• Last day of classes: April 28
• Reading Days: April 29-May 2 
• Final Examinations: May 3-11
• Spring Term Ends: May 11

Additional EPID spring courses can be found here.

Course Descriptions

Registration Form (PDF)

Independent Study Form (DOC)

Epidemiology Course Schedule: Fall 2020

•  First day of classes: September 1 (check dates for each class)
• Labor Day: September 7
• Course selection and drop period (for graduate students) ends (tbd) 
• Fall Term Break October 1-4
• Thanksgiving Break November 26-29
• Last day of classes December 10
• Reading days December 11-14
• Final Examinations December 15-20
• Fall term ends December 22

Additional EPID fall courses can be found here.

Course Descriptions

Registration Form (PDF)

Independent Study Form (DOC)

Epidemiology Course Schedule: Spring 2020

• First day of classes: January 15
• Martin Luther King, Jr Day: January 20
• Last day to add/drop course for PhD Students: February 24
• Last day to add/drop course for MS Students: January 28
• Spring Term Break: March 7-15
• Last day of classes: April 29
• Reading days: April 30 - May 3
• Final Examinations: May 4-12
* Spring term ends May 12
• Commencement: May 18

For additional EPID Spring courses click HERE.

Course Descriptions

Registration Form (PDF)

Independent Study Form (DOC)

Epidemiology Course Schedule: Spring 2019

(Tentative- subject to change)

• First day of classes: January 16
• Martin Luther King, Jr Day: January 21
• Last day to add/drop course for PhD Students: February 22
• Last day to add/drop course for MS Students: February 4
• Spring Term Break: March 2-10
• Last day of classes: May 1
• Reading days: May 2-3
• Final Examinations: May 6-14
* Spring term ends May 14
• Commencement, May 20

For additional EPID Spring courses click HERE.
Course Descriptions

Registration Form (PDF)

Independent Study Form (DOC)

Epidemiology Course Schedule: Fall 2018

(Tentative- subject to change)

First Day of classes is August 28 (check each class for start date)
Labor Day September 3
Course selection and drop period (for graduate students) ends September 17
Fall Term Break October 4-7 (classes resume on October 8)
Thanksgiving Break November 22-25 (classes resume on November 26)
Last day of classes December 10
Reading days December 11-12
Final Examinations December 13-20
Fall term ends December 20

For additional EPID fall courses click HERE.

Course Descriptions

Registration Form (PDF)

Independent Study Form (DOC)

Course Plan Form (PDF)

Epidemiology Course Schedule: Spring 2018

(Tentative- subject to change)

First day of classe is January 10
Martin Luther King, Jr Day, January 15
Last day to add/drop course (Grad Students), January 29
Spring Term Break, March 3-11
Last day of classes, April 25
Reading days, April 26-27
Final Examinations, April 30-May 8
Fall term ends May 8
Commencement, May 14

For additional EPID Spring courses click HERE.
Course Descriptions

Registration Form (PDF)

Independent Study Form (DOC)

Epidemiology Course Schedule: Fall 2017

(Tentative- subject to change)

Labor Day September 4 
Course selection and drop period (for graduate students) ends September 18 
Fall Term Break October 5-8 (classes resume on October 9)
Thanksgiving Break November 23-26 (classes resume on November 27)
Last day of classes December 11
Reading days December 12-13
Final Examinations December 14-21
Fall term ends December 21

For additional EPID fall courses click HERE.

Course Descriptions

Registration Form (PDF)

Independent Study Form (DOC)

Course Plan Form (PDF)

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BSTA 6100: Biostatistics for Epidemiologic Research

• Fall term
• 1 course unit
•  Co-requisite: EPID 7010

Description: This one-semester course is designed to provide a strong foundation in biostatistical methods for epidemiologic research, intended for students entering a PhD program in epidemiology. Covered topics include introductory probability theory, estimands, large-sample theory, hypothesis testing, confidence intervals, linear regression, generalized linear models, models for correlated data, and survival analysis, all with a throughline of likelihood-based inference. The course will consist of interactive lectures and labs designed to develop hands-on analytic skills. Prerequisite: Single variable calculus, Prior coursework in statistics at the undergraduate level

EPID 7000: Doctoral Seminar in Epidemiology

• Spring term
• 1 course unit
•  Prequisite: Instructor permission.

The course is intended to meet the needs of PhD students over the entire program from the coursework phase through the dissertation defense, and is intended to optimize cross-fertilization between the students at all phases of their program. Restricted to Epidemiology Doctoral Students. 

EPID 7010: Introduction to Epidemiologic Research

• Fall term
• 1 course unit
•  Prequisite: Quantitative proficiency. Knowledge and/or experience in working in biomedical research.

Description: This course is intended to provide in-depth, exposure to the theory and methods of epidemiologic research. Topics to be covered include causal inference, measures of disease frequency andassociation, study design, bias and confounding, validity, and epidemiologic analysis.

EPID 7020: Advanced Topics in Epidemiologic Research

• Spring Term
• 1 course unit
• Prequisite: EPID 526/527 or equivalent; EPID 701; instructor permission

Description: The overarching goal of this course is to expose doctoral students in epidemiology to advanced epidemiologic and statistical research methods and theories that are limitedly or not otherwise covered in courses available in the curriculum. Topics that will be covered include reporting guidelines and best practices for reporting statistical methods and results, handling missing data, purposeful selection and application of propensity scores, selected topics in longitudinal and clustered data analysis, contemporary topics in statistical inference and use of pvalues and other Frequentist statistical methods, Bayesian theory and inference, and topics selected in collaboration with students and the Graduate Group in Epidemiology and Biostatistics (GGEB) each term. This course is intended for doctoral students in the PhD program in Epidemiology. However, students from other graduate groups are welcome, as long as they meet the prerequisites; such students are welcome during any year of study.

EPID 7040: Methods for Social Epidemiologic Research

• Spring Term
• 1 course unit
• Prequisite: Instructor permission

Description:  Epidemiology is fundamentally an applied social science, where we develop and apply quantitative and qualitative methods to characterize health-related exposures and outcomes in populations. Social epidemiology, as one branch of the field, seeks to leverage the social sciences to extend those characterizations to include social, behavioral, and environmental factors to more fully understand the social determinants of those exposures and outcomes, and to identify and evaluate interventions to reduce health disparities. This course is intended to provide students in epidemiology, biostatistics, and other disciplines with an in-depth introduction to the principles and methods of social epidemiology.

EPID 7050: Nutritional Epidemiology

• Spring Term
• 1 course unit
• Prequisite: EPID 7010, EPID 5100, PUBH502, or equivalent; permission of course director. 

Description: This course introduces students to key concepts and methods in Nutritional Epidemiology to equip them with the tools needed to design, analyze, and critically evaluate population-based nutrition research. The course also reviews several specific diet/disease relationships, integrating information from secular trends, cohort studies, clinical trials, and animal experiments. Knowledge in nutrition is useful but not required. Prerequisites include introductory epidemiology.

EPID 7110: Environmental Epidemiology

• Spring Term
• 1 course unit
• Prequisite: instructor permission

Description: Environmental Epidemiology is an advanced epidemiology course that addresses epidemiological research methods used to study environmental exposures from air pollution to heavy metals, and from industrial pollutants to consumer product chemicals. The course will provide an overview of major study designs in environmental epidemiology, including cohort studies, panel studies, natural experiments, randomized controlled trials, time-series, and case-crossover studies. The course will discuss disease outcomes related to environmental exposures, including cancer and diseases of cardiovascular, respiratory, urinary, reproductive, and nervous systems. Case studies in environmental epidemiology will be discussed to provide details of research methods and findings. 

EPID 7012:  Nutritional Epidemiology

• Spring Term
• 1 course unit

Description: This course introduces students to key concepts and methods in Nutritional Epidemiology to equip them with the tools needed to design, analyze, and critically evaluate population-based nutrition research. The course also reviews several specific diet/disease relationships, integrating information from secular trends, cohort studies, clinical trials, and animal experiments. Knowledge in nutrition is useful but not required. 

EPID 7013:  Epidemiology of Substance Use and Related Complex Health Behaviors

• Spring Term
• 1 course unit

Description: The course presents an overview of the epidemiology of substance use and related complex health behaviors within a public health framework. Students will explore a range of contributors to substance use, considering mechanisms ranging from biological to societal/structural. The course will introduce the historical background of the “war on drugs” and racial underpinnings of policies towards substance use;mental health definitions of addiction; substance use prevention strategies; substance use policies and their impacts; intervention and treatment approaches; barriers to treatment access and adherence; and structural risk factors for substance use and related problems. Students will critically evaluate methods for studying hidden and hard-to-reach populations—including sources of bias, measurement issues, and ethical considerations—as well as explore new and emerging innovations in studying substance use. We will also discuss the application of cross-cutting methods to the study of substance use (e.g., population surveillance and surveys, case-control studies, quasi-experimental designs such as difference-in-difference, mixed/qualitative methods).

More Course Descriptions for Epidemiology

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BiostatisticsCourse Schedule: Spring 2025

• First day of classes: January 15 (Monday Classes)
• MLK, Jr. Day (no classes): January 20
• Course Selection Period Ends: January 28
• Spring Break: March 8-16 
• Last day of classes: April 30
• Reading Days: May 1-4
• Final Examinations: May 5-13
• Spring Term Ends: May 13
• Commencement: May 19

Biostatistics Course Schedule: Fall 2024

• First day of classes: Tuesday, August 27 
• Labor Day (no classes): September 2
• Fall Term Break: October 3-6 (observance up to GGEB instructors)
• Thursday-Friday class schedule on Tuesday-Wednesday: November 26-27
• Thanksgiving Break: November 28- December 1 
• Last day of classes: December 9
• Reading Days: December 10-11 
• Final Examinations: December 12-19
• Fall Term Ends: December 19

Biostatistics Course Schedule: Spring 2024

• First day of classes: January 18
• Course Selection Period Ends: January 31
• Course drop period (for graduate students) ends: February 27
• Spring Break: March 2-10
• Last day of classes: May 1
• Reading Days: May 2-5 
• Final Examinations: May 6-14
• Spring Term Ends: May 14
• Commencement: May 20

Biostatistics Course Schedule: Fall 2023

• First day of classes: August 29 
• Labor Day (no classes): September 4th
• Fall Term Break: October 12-15 (observance up to GGEB instructors)
• Thursday-Friday class schedule on Tuesday-Wednesday: November 21-22
• Thanksgiving Break: November 23-26 
• Last day of classes: December 11
• Reading Days: December 12-13 
• Final Examinations: December 14-21
• Fall Term Ends: December 21 

Biostatistics Course Descriptions
Academic Calendar 2019-2022
Graduation Calendar

Biostatistics Course Schedule: Spring 2023

• First day of classes: January 11 (Monday classes meet Wednesday)
• MLK, Jr. Day: January 16 (no class)
• Course Selection Period Ends: January 24
• Course drop period (for graduate students) ends: February 20
• Spring Break: March 4-12 
• Last day of classes: April 26
• Reading Days: April 27-30 
• Final Examinations: May 1-9
• Spring Term Ends: May 9
• Commencement: May 15

Biostatistics Course Descriptions
Academic Calendar 2019-2022
Graduation Calendar

Biostatistics Course Schedule: Fall 2022

• First day of classes: August 30 (check dates for each class)
• Labor Day: September 5 (no class)
• Course drop period (for graduate students) ends: TBD
• Fall Term Break: October 14-17 (GGEB does not observe)
• Thursday/Friday Class Scheduled on Tuesday/Wednesday: November 22 and 23
• Thanksgiving Break: November 24-27
• Last day of classes: December 12
• Reading Days: December 13-14 
• Final Examinations: December 15-22
• Fall Term Ends: December 22

Biostatistics Course Descriptions
Academic Calendar 2019-2022
Graduation Calendar

Biostatistics Course Schedule: Spring 2022

•  First day of classes: January 12 (Monday schedule)
•  MLK Jr. Day: January 17th
• Course drop period (for graduate students) ends: February 21 
• Spring Break: March 5 - 13
• Last day of classes: April 28
• Reading Days: April 29-May 1 
• Final Examinations: May 2-10
• Spring Term Ends: May 10
• Commencement: May 16
   

Biostatistics Course Schedule: Fall 2021

•  First day of classes: August 31 (check dates for each class)
• Labor Day: September 6
• Course drop period (for graduate students) ends: TBD
• Fall Term Break: October 14-17
• Thursday/Friday Class Scheduled on Tuesday/Wednesday: November 23 and 24
• Last day of classes: December 10
• Reading Days: December 11-14 
• Final Examinations: December 15-22
• Fall Term Ends: December 22

Course Descriptions

Registration Form

Independent Study Form (doc)

Biostatistics Course Schedule: Spring 2021

•  First day of classes: January 20 (check dates for each class)
• Course drop period (for graduate students) ends: February 22 
• Engagement Day: February 12th
• Spring Break: March 10 & 11
• Engagement Days: March 30th and April 12th
• Last day of classes: April 28
• Reading Days: April 29-May 2 
• Final Examinations: May 3-11
• Spring Term Ends: May 11
• Commencement: May 17
   

Course Descriptions

Registration Form

Independent Study Form (doc)

Biostatistics Course Schedule: Fall 2020

•  First day of classes: September 1 (check dates for each class)
• Labor Day: September 7
• Course selection and drop period (for graduate students) ends (tbd) 
• Fall Term Break October 1-4
• Thanksgiving Break November 26-29
• Last day of classes December 10
• Reading days December 11-14
• Final Examinations December 15-20
• Fall term ends December 22

Course Descriptions

Registration Form

Independent Study Form (doc)

Biostatistics Course Schedule: Spring 2020

• First day of classes: January 15
• Martin Luther King, Jr Day: January 20
• Last day to add/drop course for PhD Students: February 24
• Last day to add/drop course for MS Students: January 28
• Spring Term Break: March 7-15
• Last day of classes: April 29
• Reading days: April 30 - May 3
• Final Examinations: May 4-12
* Spring term ends May 12
• Commencement: May 18

Course Descriptions

Registration Form

Independent Study Form (doc)

Biostatistics Course Schedule: Spring 2019

(Tentative- subject to change)

• First day of classes: January 16
• Martin Luther King, Jr Day: January 21
• Last day to add/drop course for PhD Students: February 22
• Last day to add/drop course for MS Students: February 4
• Spring Term Break: March 2-10
• Last day of classes: May 1
• Reading days: May 2-3
• Final Examinations: May 6-14
* Spring term ends May 14
• Commencement, May 20

Course Descriptions

Registration Form (pdf)

Independent Study Form (doc)

Biostatistics Course Schedule: Fall 2018

(Tentative- subject to change)

First day of classes is August 28 (check each class for start date)
Labor Day September 3
First day of Biostatistics classes is September 4
Course selection and drop period (for graduate students) ends September 17 
Fall Term Break October 4-7 (classes resume on October 8)
Thanksgiving Break November 22-25 (classes resume on November 26)
Last day of classes December 10
Reading days December 11-12
Final Examinations December 13-20
Fall term ends December 20

Course Descriptions
Registration Form (PDF)
Independent Study Form(PDF)

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Courses in Biostatistics and Statistics

The Center for Clinical Epidemiology and Biostatistics, the Department of Biostatistics and Epidemiology, and the Graduate Group in Epidemiology and Biostatistics offer a wide range of courses; a brief description of current offerings is provided below. Not all courses are offered every year. The program may revise these courses over time; the descriptions given here are for guidance only.

BSTA 5110: Biostatistics in Practice I

• Fall/Spring Term (offered to Biostatistics students only)
• 1 credit unit
• Prerequisites: Open to Biostatistics students only.

BSTA 513: Measurement of Health in Epidemiology (EPID 542) 

BSTA 514: Clinical Economics and Clinical Decision Making (EPID 550)

BSTA 550: Applied Regression and Analysis of Variance (STAT 500)

BSTA 6200: Probability

• Fall term
• 1.0 credit unit
• Prerequisites: Two semesters of calculus (through multivariable calculus), linear algebra; permission of instructor.

Description: This core course covers elements of (non-measure theoretic) probability necessary for the further study of statistics and biostatistics. Topics include set theory, axioms of probability, counting arguments, conditional probability, random variables and distributions, expectations, generating functions, families of distributions, joint and marginal distributions, hierarchical models, covariance and correlation, random sampling, sampling properties of statistics, modes of convergence, and random number generation.

BSTA 6210: Statistical Inference I

• Spring term
• 1.0 credit unit
• Prerequisites: BSTA 620; permission of instructor.

Description: This class will cover the fundamental concepts of statistical inference. Topics include sufficiency, consistency, finding and evaluating point estimators, finding and evaluating interval estimators, hypothesis testing, and asymptotic evaluations for point and interval estimation.

BSTA 6220: Statistical Inference II

• Fall/Spring term
• 1.0 credit unit
• Prerequisites: BSTA 620; permission of instructor.
Description: This class will cover the fundamental concepts of statistical inference. Topics include sufficiency, consistency, finding and evaluating point estimators, finding and evaluating interval estimators, hypothesis testing, and asymptotic evaluations for point and interval estimation.

BSTA 6300: Statistical Methods and Data Analysis I

• Fall term
• 1.0 credit unit
• Prerequisites: Multivariable calculus and linear algebra, BSTA 620 (may be taken concurrently); permission of instructor.

Description: This first course in statistical methods for data analysis is aimed at first-year Biostatistics students. It focuses on the analysis of continuous data. Topics include descriptive statistics (measures of central tendency and dispersion, shapes of distributions, graphical representations of distributions, transformations, and testing for goodness of fit); populations and sampling (hypotheses of differences and equivalence, statistical errors); one- and two-sample t tests; analysis of variance; correlation; nonparametric tests on means and correlations; estimation (confidence intervals and robust methods); categorical data analysis (proportions; statistics and test for comparing proportions; test for matched samples; study design); and regression modeling (simple linear regression, multiple regression, model fitting and testing, partial correlation, residuals, multicollinearity). Examples of medical and biologic data will be used throughout the course, and use of computer software demonstrated.

BSTA 6320: Statistical Methods for Categorical and Survival Data 

• Spring term
• 1.0 credit unit
• Prerequisites: Linear algebra, calculus, BSTA 630, BSTA 620, BSTA 621 (may be taken concurrently); permission of instructor.

Description: This is the second half of the methods sequence, where the focus shifts to methods for categorical and survival data. Topics in categorical include defining rates; incidence and prevalence; the chi-squared test; Fisher's exact test and its extension; relative risk and odds-ratio; sensitivity; specificity; predictive values; logistic regression with goodness of fit tests; ROC curves; the Mantel-Haenszel test; McNemar's test; the Poisson model; and the Kappa statistic. Survival analysis will include defining the survival curve, censoring, and the hazard function; the Kaplan-Meier estimate, Greenwood's formula and confidence bands; the log rank test; and Cox's proportional hazards regression model. Examples of medical and biologic data will be used throughout the course, and use of computer software demonstrated.

BSTA 6510: Introduction to Linear Models and Generalized Linear Models

• Spring term
• 1.0 credit unit
• Prerequisites: Linear algebra, calculus, BSTA 620, BSTA 630. BSTA 621 and BSTA 632 (may be taken concurrently); permission of instructor.

Description: This course extends the content on linear models in BSTA 630 and BSTA 631 to more advanced concepts and applications of linear models. Topics include the matrix approach to linear models including regression and analysis of variance; multiple linear regression, collinearity diagnostics; multiple comparisons; fitting strategies; simple experimental designs (block designs, split plot); and prediction. In addition, generalized linear models will be introduced with emphasis on the binomial, logit and Poisson log-linear models. Applications of methods to example datasets will be emphasized.

BSTA 6560: Longitudinal Data Analysis

• Fall term
• 1.0 credit unit
• Prerequisites: BSTA 621, BSTA 631 or 632, BSTA 651, BSTA 653 or 754; permission of instructor.

Description: This course covers both the applied aspects and methods developments in longitudinal data analysis. In the first part, we review the properties of the multivariate normal distribution and cover basic methods in longitudinal data analysis, such as exploratory data analysis, two-stage analysis and mixed-effects models. Focus is on the linear mixed-effects models, where we cover restricted maximum likelihood estimation, estimation and inference for fixed and random effects and models for serial correlations. We will also coverBayesian inference for linear mixed-effects models.The second part covers advanced topics, including nonlinear mixed-effects models, GEE, generalized linear mixed-effects models, nonparametric longitudinal models, functional mixed-effects models, and joint modeling of longitudinal data and the dropout mechanism. 

BSTA 6600: Design of Observational Studies

• Fall term
• 0.5 credit unit
• Prerequisites: BSTA 621, BSTA 631 or BSTA 632, BSTA 651; permission of instructor.

Description: This course will cover statistical methods for the design and analysis of observational studies.  Topics for the course will include epidemiologic study designs, issues of confounding and hidden bias, matching methods, propensity score methods, sensitivity analysis, and instrumental variables. Case studies in biomedical researchwill be presented as illustrations.

BSTA 6610: Design of Interventional Studies

• Fall term
• 0.5 credit unit
• Prerequisites: BSTA 621, BSTA 631 or BSTA 632; permission of instructor.

Description: This course is designed for graduate students in statistics or biostatistics interested in the statistical methodology underlying the design, conduct, and analysis of clinical trials and related interventional studies. General topics include designs for various types of clinical trials (Phase I, II, III), endpoints and control groups, sample size determination, and sequential methods and adaptive design. Regulatory and ethical issues will also be covered.

BSTA 6700: Programming and Computation for Biomedical Data Science

• Spring term
• 1.0 credit unit
• Prerequisites: BSTA 651,BSTA 620, BSTA 621 or equivalents, or permission of instructor.

Description: This course concentrates on programming and computational tools that are useful for statistical research and data science practice. Programming will mainly be taught in R and Python with a focus on performance and efficiency, including parallelization techniques. Select computational topics will include computer arithmetic; algorithms and complexity; random number generation; simulation design; bootstrap methods; numerical analysis and optimization; numerical integration; and a number of advanced topics.

BSTA 7510: Statistical Methods for Neuroimaging

• Spring term
• 1.0 credit unit
• Prerequisites: BSTA 621, BSTA 651; permission of instructor.

Description:This course is intended for students interested in both statistical methodology, and the process of developing this methodology, for the field of neuroimaging. This will include quantitative techniques that allow for inference and prediction from ultra-high dimensional and complex images. In this course, basics of imaging neuroscience and preprocessing will be covered to provide students with requisite knowledge to develop the next generation of statistical approaches for imaging studies. High-performance computational neuroscience tools and approaches for voxel- and region-level analyses will be studied. The multiple testing problem will be discussed, and the state-of-the art in the area will be examined. Finally, the course will end with a detailed study of multivariate pattern analysis, which aims to harness patterns in images to identify disease effects and provide sensitive and specific biomarkers. The student will be evaluated based on 3 homework assignments and a final in-class presentation.

BSTA 7540: Advanced Survival Analysis

• Fall term
• 1.0 credit unit
• Prerequisites: BSTA 622 (may be taken concurrently); permission of instructor.

Description: This advanced survival analysis course will cover statistical theory in counting processes, large sample theory using martingales, and other state of the art theoretical concepts useful in modern survival analysis research. Examples in deriving rank-based tests and Cox regression models as well as their asymptotic properties will be demonstrated using these theoretical concepts. Additional potential topics may include competing risk, recurrent event analysis, multivariate failure time analysis, joint modeling of survival and longitudinal data, sample size calculations, multistate models, and complex sampling schemes involving failure time data.

BSTA 770: Nonparametric Inference (STAT 915)

BSTA 7710: Applied Bayesian Analysis

• Spring term
• 1.0 credit unit
• Prerequisites: BSTA 620, BSTA 621, BSTA 651; permission of instructor. 

Description: This course introduces Bayesian methods from philosophical, theoretical, and practical perspectives. These methods are compared and contrasted with alternatives, such as maximum likelihood and semiparametric methods. Core topics include Bayes' theorem, the likelihood principle, selection of prior distributions (both informative and non-informative), and computational methods for sampling from the posterior distributions. Bayesian approaches to linear models, generalized linear models, and survival models are presented, along with methods for model checking and model choice such as posterior predictive distributions and Bayes factors. Computational methods include MCMC, Gibbs sampling, metropolis algorithms, and slice sampling. Advanced topics include Bayesian non-parametric models and data augmentation. The course emphasizes the development and estimation of hierarchical models as a means of modeling complicated real-world problems.

BSTA 7740: Statistical Methods for Evaluating Diagnostic Tests

• Fall term
• 1.0 credit unit
• Prerequisites: BSTA 621 or equivalent; permission of instructor. 

Description: Topics include estimation of ROC curves; comparison of multiple diagnostic tests; development of diagnostic tests using predictive models; effects of measurement errors; random-effects models for multi-reader studies; verification bias in disease classification; methods for time-dependent disease classifications; study design; related software; meta-analyses for diagnostic test data; and current topics in the statistical literature.

BSTA 7750: Sample Survey Methods (STAT 920)

BSTA 7790: Semiparametric Inferences and Biostatistics

• Spring term (Course not offered every year)
• 1.0 credit unit
• Prerequisites: The course is designed for students in biostatistics, statistics, or other strongly quantitative disciplines. BSTA 621/622 or equivalent; ability to program in R/S-Plus, SAS, Stata or Matlab; permission of the instructor. 

Description: This course will expose students to semiparametric inference theory through its applications to cutting-edge research topics in biostatistics, including two-phase design problems and modeling problems in genetic epidemiology. Thus, this course will benefit those who wish to advance their theoretical statistical training, those who wish to explore biostatistics research in the area of two-phase design problems and in genetic epidemiology, and those who wish to deepen their understanding of commonly used semiparametric biostatistical methods such as partial likelihood inference for Cox regression and the prospective analysis of retrospective case-control studies.

BSTA 7800: The Science of Science and Innovation

• Spring term 
• 1.0 credit unit
• Prerequisites: Instructor permission;

Description: The increasing burden of knowledge in biomedical science has led training and coursework to focus on the many trees within a specific area of research. While understandable, this narrowed scope means that scientists themselves are often unaware of historical, economic, and social forces that structure the enterprise in which they work. This course aims to illuminate these dynamics. Tapping into the many emerging metasciences—the science of science, economics of science, philosophy of science, etc—we will embark on a slow zoom in from a 1000 foot view, moving gradually from the perspective of governments, to funders, to practitioners, to trainees.

BSTA 7810: Asymptotic Theory with Biomedical and Psychosocial Applications

• Fall term (Course not offered every year)
• 1.0 credit unit
• Instructor (s): TBA
• Prerequisites: BSTA 621, BSTA 622, BSTA 630, BSTA 631 or BSTA 632, BSTA 651; permission of instructor.

Description: This course is an introduction to the asymptotic theory of statistics, with an array of applications to motivate as well as demonstrate its utility in addressing problems in biomedicine and psychosocial research. Notions of convergence of random sequences and common asymptotic techniques are introduced without measure theory. In addition to classical likelihood-based asymptotic theory, this course also focuses on distribution-free inference from estimating equations and U-statistics. Examples from AIDS, genetic, and psychosocial research are presented to motivate the methods development and to demonstrate the utility of the asymptotic theory.

BSTA 7820: Statistical Methods for Incomplete Data

• Spring term (Course not offered every year)
• 1.0 credit unit
• Prerequisites: BSTA 621 required; BSTA 670 recommended; permission of instructor.

Description: This course reviews the theory and methodology of incomplete data, covering ignorability and the coarse-data model, including MAR, MCAR and their generalizations; computational methods such as the EM algorithm and its extensions; methods for handling missing data in commonly used models such as the generalized linear model and the normal mixed model; methods based on imputation; diagnostics for sensitivity to nonignorability; and nonignorable modeling and current topics.

BSTA 7830: Multivariate and Functional Data Analysis

• Fall term
• 1.0 credit unit
• Prerequisites: BSTA 621, BSTA 651, BSTA 656; permission of instructor.

Description: This course covers both the classical theory and recent methods for multivariate exploratory analysis, as well as techniques for handling functional data. The first part reviews classical multivariate exploratory methods such as principal component analysis, factor analysis, cluster analysis and discriminant analysis, as well more recent methods, such as structural equations models, neural networks and classification trees. The second part covers the more advanced topic of functional data analysis, including graphical representations, principal component analysis and linear models for functional data.

BSTA 7840: Analysis of Biokinetic Data 

• Fall term (Note: Course no longer offered)
• 0.5 credit unit
• Prerequisites: Introductory statistics including regression and hypothesis testing; EPID 520, BSTA 630 or equivalent; permission of instructor.

Description: The time-course of a drug monitored via circulation samples gives us a comprehensive account of the number and sizes of body pools within which the drug distributes before its eventual elimination. Furthermore, the pattern of change of the time-course with increasing drug doses will expose the nature of the mechanisms facilitating that transport and metabolism. How these features are elucidated falls under the general topic of Compartmental Analysis, and the tools and technique of kinetics as well as those of drug dynamics form a part of this topic investigating 'the analysis of biokinetic data'. Additionally we will be exploring how metabolic challenges, such as the glucose challenge, the TRH challenge, and the epinephrine challenge expose aspects of the functionality of their targeted tissues, and, most specifically, we will show how indices relating to insulin resistance are derived.

BSTA 7850: Statistical Methods for Genomic Data Analysis

• Spring term
• 1.0 credit unit
• Prerequisites: BSTA 620, BSTA 621, these courses can be taken concurrently with this course; permission of the instructor.

Description: This course covers statistical, probabilistic and computational methods for analyzing high-throughput genomic data. With the advent of inexpensive DNA sequencing, statistical genetics is undergoing the transition to big data. The following materials will be selectively covered. Basics of Molecular Biology and Population Genetics; Large-scale inference, empirical Bayes methods, False discovery rate theory and applications to differential expression analysis, RNA-seq data analysis; Network-based analysis of genomic data and Hidden Markov random field models; Sparse segment identification in high dimensional settings with applications to copy number variation analysis using SNP chip data and next generation sequencing data; High dimensional regression and regularization methods in genomics; Genetic networks and Gaussian graphical models, Conditional Gaussian graphical models, Causal inference and directed graphs; Analysis of microbiome data and high dimensional compositional data; Kernel methods and analysis of rare variants; Other miscellaneous topics in analysis of next generation sequencing data (e.g. ChIP-seq data, epigenomics data); Bioconductor/R programs for genomic data analysis.

BSTA 7860: Advanced Topics in Clinical Trials

• Spring term
• 0.5 credit unit
• Prerequisites: BSTA 661; permission of instructor.

Description: This course will cover in some depth selected topics of interest in clinical trials that are discussed only minimally in the introductory clinical trials courses. Topics may include methods of treatment allocation and blinding, sequential and/or adaptive trial designs, methods of handling missing data, design of active control/noninferiority trials, constructed endpoints, and other topics based on interest of registrants.

BSTA 7870: Methods for Statistical Genetics and Genomics in Complex Human Disease

• Spring term
• 1.0 credit unit
• Prerequisites: Introductory graduate-level courses in statistics (such as BSTA 630-632 or EPID 520-521) are required; or permission of the instructor.

Description: This is an advanced elective course for graduate students in Biostatistics, Statistics, Epidemiology, Bioinformatics, Computational Biology, and other BGS disciplines. This course will cover statistical methods for the analysis of genetics and genomics data. Topics covered will include genetic linkage and association analysis, analysis of next-generation sequencing data, including those generated from DNA sequencing and RNA sequencing experiments. Students will be exposed to the latest statistical methodology and computer tools on genetic and genomic data analysis. They will also read and evaluate current statistical genetics and genomics literature.

BSTA 7880: Functional Data Analysis

• Spring term
• 1.0 credit unit
• Prerequisites: BSTA 621 and BSTA 651; permission from the instructor.

Description: This course will cover both the basic techniques in functional data analysis and the latest methodological developments in the area. The first half of the course will cover graphical representations, smoothing techniques, curve registration, functional linear models, functional principal component and discriminant analysis. The first half will follow the book by Ramsay and Silverman (2005). The first half aims to prepare the students to analyze functional data. The second half will cover several special topics of the recent development. We will cover around twenty papers in the second half. Each student is expected to complete a term project at the end. The ideal term project can potentially lead to a dissertation topic.

BSTA 7890: Big Data

• Fall term
• 1.0 credit unit
• Prerequisites: BSTA 621 and BSTA 622.  BSTA 622 can be taken concurrently.

Description: Selected topics from public health and biomedical research where "Big data" are being collected and methods are being developed and applied, together with some core statistical methods in high dimensional data analysis. Topics include dimension reduction, detection of novel association in large datasets, regularization and high dimensional regression, ensemble learning and prediction, kernel methods, deep learning and network analysis. R programs will be used throughout the course, other standalone programs will also be used.

BSTA 7900: Causal Inference in Biomedical Research

• Fall term
• 1.0 credit unit
• Prerequisites: BSTA 621, BSTA 622; permission of instructor.

Description: This course considers approaches to defining and estimating causal effects in various settings. The potential-outcomes approach provides the framework for the concepts of causality developed here, although we will briefly consider alternatives. Topics considered include: the definition of effects of scalar or point treatments; nonparametric bounds on effects; identifying assumptions and estimation in simple randomized trials and observational studies; alternative methods of inference and controlling confounding; propensity scores; sensitivity analysis for unmeasured confounding; graphical models; instrumental variables estimation; joint effects of multiple treatments; direct and indirect effects; intermediate variables and effect modification; randomized trials with simple noncompliance; principal stratification; effects of time-varying treatments; time-varying confounding in observational studies and randomized trials; nonparametric inference for joint effects of treatments; marginal structural models; and structural nested models.

BSTA 7980: Advanced Topics in Biostatistics I

• Spring term
• 0.5 credit unit
• Prerequisites: permission of instructor;

Description: This seminar will be taken by doctoral candidates after the completion of most of their coursework. Topics in biostatistical methodology will vary from year to year. Methodology related to clinical trials, missing data, functional data analysis, generalized linear models, statistical genetics, advances in Bayesian methodology are examples of areas that may be covered.

BSTA 799: Advanced Topics in Biostatistics II

• Fall/Spring term
• 0.5 credit unit
• Prerequisites: permission of instructor;

Description: This seminar will be taken by doctoral candidates after the completion of most of their coursework. Topics in biostatistical methodology will vary from year to year. Methodology related to clinical trials, missing data, functional data analysis, generalized linear models, statistical genetics, advances in Bayesian methodology are examples of areas that may be covered.

BSTA 812: Seminar in Probability Theory (STAT 955)

BSTA 820: Statistical Inference III (STAT 552)

BSTA 852: Forecasting and Time Series (STAT 910)

BSTA 870: Seminar in Advanced Applications of Statistics (STAT 991)

BSTA 920: Guided Tutorial: Research (0.5 - 3.0 course units)

BSTA 995: Dissertation Research (0.5 – 3.0 course units)

BSTA 999: Independent Study (0.5 - 1.0 course unit)

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