Courses

Elective Courses

A variety of elective courses are offered each semester and are open to matriculated students and Career Enhancement students. All Master’s degree students are required to complete 8 credits of elective coursework.

Study Design and Methods for Comparative Effectiveness Research
Credits:
Three
Instructor(s): 
Alvin Mushlin, Brian Carroll

This course will cover the conceptual underpinnings and policy context of comparative effectiveness research (CER), highlighting key controversies. It will provide students with an understanding of the analytic methods and data resources used to conduct comparative effectiveness research. Topics that are likely to be discussed include observational studies, risk adjustment, propensity score matching, instrumental variables, systematic reviews, and the use of clinical registries and Medicare claims data. Students will learn why comparative research has come to prominence, what makes good comparative effectiveness research, and how to conduct comparative effectiveness research. Students will learn the main methods used in comparative effectiveness research and the advantages and disadvantages of each. The course will not be a statistics course or how-to course. Course sessions will consist of both lectures from the instructors and experts on selected topics as well as student presentations.

Cost-Effectiveness Analysis
Credits:
Three
Instructor(s): 
Alvin Mushlin, Brian Carroll

The cost effectiveness analysis course is a 2-part course. The first part provides an overview of techniques used to understand medical decision making under uncertainty. Participants will learn how to structure decision analysis questions, construct decision trees, and analyze outcomes using probability. The second part provides an in-depth exposure to techniques used to conduct economic evaluations of health care technologies and programs. Participants learn how to critique economic evaluations using cost-effectiveness approaches and are introduced to tools they can use to apply these techniques in their own research projects. Prerequisites: Biostatistics I or Introduction to Biostatistics.

Participatory Design for Digital Making
Credits:
Three
Instructor(s): 
Niti Parikh

This is a collaborative workshop where participants (seniors and graduate students from Cornell Tech and Weill Cornell CTSC) work on a prototype for a real-world problem that is worth investigating around digital fabrication. This workshop is an introduction to concepts and methods in design and making with digital fabrication tools while working in intergenerational and intercultural teams. The ability to digitally fabricate parts and whole pieces directly from our computers or design files used to be an exotic and expensive option, but 3d printing has fast become the preferred medium to allow easily adaptable ideas to develop from concept to creation quickly, at a relatively low cost. Not limited to just 3d Printing this course will focus in the area of materials and making, simulation, computational design and abilities to co-create in a team of diverse disciplinary backgrounds.

Sociocultural Barriers in STEM
Credits:
1
Instructor(s): 
Sushmita Mukherjee, Monica Guzman

The course is designed to give graduate students an introduction to sociological literature surrounding inequities in STEM. We will review the origins of implicit bias, racism, sexism, and exclusion in STEM as well as the data and bias against certain populations, and also present bias-reducing strategies to achieve equity and inclusion in STEM. We will discuss the historical context of bias and exclusion in science, read from and discuss the primary literature to understand the science of bias and why it is present and how it has continued to persist across the Science, Technology, Engineering, and Mathematics (STEM) fields, and identify actionable items to address and overcome these issues.

Data Structures and Algorithms for Computational Biology
Credits:
Four
Instructor(s): 
Iman Hajirasouliha

This is a unique graduate course, which addresses fundamental data structures and algorithms that are being applied in modern computational biology. The students will focus on algorithmic problem solving and learn several algorithmic techniques. Students will also learn how to design and apply data structures and algorithms to state-of-the-art biology problems such as large-scale genome sequence analysis. Not currently offered.

Science of Team Science: Practical Approaches to Working Effectively
Credits:
One
Instructor(s): 
Bales, Michael

This course provides students with an overview of the emerging research field of the Science of Team Science, with a focus on the knowledge and skills that support effective scientific collaboration. Topics include identifying collaborators, working with individuals from different disciplines, conflict prevention and management, negotiating funding and co-authorship, and evidence-based strategies for effective team leadership. The course will also cover considerations for working with geographically distributed collaborators, including the use of tools and technologies to support remote collaboration.

Fundamental Immunology and Microbiology
Credits:
Part 1 - 5.5, Part 2 - TBD
Instructor(s): 
Ming Li, PhD

This is a two-part series that will provide a fundamental understanding of immunology.  Immunology I will give a comprehensive overview of basic immunology beginning with innate immune responses followed by a study of the main aspects of acquired immunity. Important topics include the following: organization of lymphoid tissues and immune cell migration, cellular and molecular aspects of innate immunity, specific interactions of target cells and T cells that are regulated by the MHC molecule and peptide antigens on the target cell and the antigen specific T cell receptor; generation and molecular structure of B and T cell antigen receptors; signaling through immune receptors; the development of antigen specific T and B cells; and specific roles of some cytokines/lymphokines.  The second part of the series is Immunology II, held in the Spring semester, which focuses on aspects of T and B effector cell generation, immune response generation and regulation in the context of infection, autoimmunity, tumor immunity, and transplant. 

Introduction to R-Programming
Credits:
One
Instructor(s): 
Charlene Thomas

This is an elective course for students seeking to gain beginner-level skills in data structures, data manipulation, generating descriptive statistics, and data visualization in the R programming language and environment. Base R as well as tidyverse R coding will be covered. Previous experience with a programming language is not necessary. Applications of skills learned in this course are geared towards clinical research, but these skills are transferrable to many projects outside the scope. Prerequisites: No prior programming experience required, but some familiarity working with data in clinical research is useful.

Advanced Seminars in Ethics of Clinical Research
Credits:
One
Instructor(s): 
Inmaculada de Melo-Martin, PhD, MS

Scientific research influences all of us in various ways. Scientific knowledge transforms our lives and our societies in positive and negative ways. Science informs public policies that affect communities. A scientifically informed public is essential to well-functioning democracies. Moreover, some of us become research subjects and are yet affected by scientific research in even more intimate ways. Scientific research thus raises a variety of ethical challenges. This course explores some of these issues from a philosophical point of view. We will consider broad questions about the role of values in science, scientists’ ethical obligations, and researchers’ accountability for the societal impacts of scientific research. Our focus will be the biomedical sciences.

Advanced Statistical Methods for Observational Studies
Credits:
Two
Instructor(s): 
Paul Christos, Dr. P.H.

This course will provide trainees with an overview of statistical methods and issues related to the design and analysis of observational studies. Course objectives are as follows: understand the value of observational study design and the background for causal inference; understand the differences between randomized studies and observational studies; introduce the concept of meta-analysis and their reporting standards; learn advanced statistical methods for the analysis of different types of data (e.g., binary, censored data, complex survey data, and repeated measurements) used in observational studies and be able to implement the methods in Stata; understand how to interpret numbers in the published literature and health news properly. Prerequisite: Introduction to Biostatistics or similar course is required prior to enrollment.

Genomics Workshop
Credits:
One
Instructor(s): 
Jenny Xiang, BM

This course is designed to give the students a detailed overview of current genomics technologies and their applications in the biomedical field. The primary objectives are for the students to become familiar with the key concepts, general methodologies and experimental design of the technology, and to acquire the ability to interpret genomics data and design their own research experiments. The course will cover the experimental design, data analysis and interpretation of the next-generation sequencing data and will be delivered through a combination of lectures and tutorial.

Heart-to-Heart: Experiential Learning in Community Outreach
Credits:
One-Two
Instructor(s): 
N/A

The CTSC “Heart-to-Heart” (H2H) Campaign is a multi-institutional service program that reaches out to underserved at-risk communities throughout the metropolitan area by offering free healthcare screenings at local community sites. Volunteer physicians may earn course credit for their participation in these events as part of CTEP’s elective course titled, “Heart to Heart: Experiential Learning in Community Outreach.” Non-MDs may also enroll for credit; assigned roles are experience dependent, and assignments may be to help with registration or ushering duties. To qualify you must be an enrolled CTEP student in good standing, MDs must be a U.S. licensed MD (resident, fellow, attending, etc.), non-MDs must be employed at WCMC or one of the partner institutions. Trainees may earn 1 to 2 elective credits based on the number of sessions attended (2 sessions per credit), on the feedback collected from community attendees, and submission of a brief (1-2 page) write-up in a form of a blog post to be published on the Weill Cornell CTSC blog. The write-up should discuss this experience and how it has impacted the trainee’s research and/or perspective as a clinician. 

Introduction to Global Health
Credits:
One
Instructor(s): 
Satchit Balsari, MD, MPH

This course will introduce students to thirteen key topics in global health through 1.5-hour weekly seminars. Speakers include global health leaders from academia, policy institutions, and the private sector. The course is intended to be engaging and informative and each seminar is designed to be interactive and discussion-based. Not currently offered through CTSC.

Multi-Cultural Approaches to Community Health and Disease Prevention
Credits:
Two
Instructor(s): 
Laura Robbins, DSW, CSW, MSW

This course will provide an overview of cultural diversity and its impact on the development and implementation of health promotion policies, programs, and health services research. Not currently offered through CTSC.

Pharmacology and Drug Development
Credits:
One
Instructor(s): 
N/A, eLearning course

This course covers the drug development process from discovery to post-marketing in a 5 modular format.

This course is a prerequisite for Clinical and Translational Pharmacology.

Principles of Clinical Research and Design
Credits:
One
Instructor(s): 
N/A, eLearning course

This course is an introduction to the design of sound clinical research, including randomized trials, epidemiological studies and health economics/outcomes research, covering in a 4 modular format. Course topics covered include: Principles of Clinical Research and Design, Randomized Clinical Trials, Non-Interventional Studies – Epidemiology, and Other Non-Interventional Studies.

This course is a prerequisite for Clinical Trials Design and Analysis.

Qualitative Methods Health Research
Credits:
Three
Instructor(s): 
Czarina Behrends, Brian Carroll

This course provides an introduction to qualitative theory and methods in health research. Topics will include qualitative research theory, development of qualitative research proposals, interview approaches, qualitative analysis, mixed methods, and theoretical frameworks. The aim of this course is to develop introductory, basic skills for conducting a qualitative research study from beginning to end by providing a combination of education on qualitative theory and providing opportunities to apply that education to a semester long project that mimics a qualitative health research study. This course will use a combination of didactic lectures, discussion, and small group work.

Clinical & Translational Science Center 1300 York Ave., Box 149 New York, NY 10065