Course Descriptions

This course, taught by MSCTI Program directors and various guest lecturers, focuses on helping students develop grant and manuscript writing skills in the area of clinical and translational science across the translational science spectrum.

The focus of this course, sometimes called the "responsible conduct of research" (RCR), is on scientific integrity – both the principles and day-to-day practicalities of research ethics. Taking an interdisciplinary approach, the course’s readings and other materials draw on examples from many academic fields and can be applied to any academic or professional area of study in which research is conducted.

This course introduces students to fundamental topics in translational science related to team science and entrepreneurship. Through a series of lectures, participatory exercises, and guided discussions, students learn practical strategies for engaging in team science and developing a translational research project into an entrepreneurial endeavor.

The Capstone Project is an opportunity and a vital component in the training and hands-on exposure for MSCTI students to apply the principles and practices of clinical and translational research. Students will engage in mentored research and complete either a research thesis, produce a publishable project in the form of a submitted manuscript, or submit a grant application, and all must deliver an oral presentation summarizing the research conducted. Students are encouraged to build their Capstone Program in a way that supports their academic and professional interests as they advance in their research careers as impactful clinical and translational science investigators and professionals.

Students learn about the planning, design, analysis, and data management for clinical therapeutic and prophylactic trials. Illustrations are provided through case examples.

This course introduces the principles and methods of epidemiology, including descriptive epidemiology, environmental and other risk factors, detection of outbreaks, basic demography, and etiologic studies.

This course provides an overview of the science of genetics, including historical and modern approaches, with an emphasis on the underlying mechanisms of human genomic variation and their relation to human disease. It uses a combination of lectures and discussion of primary literature. After completing the course, students will be able to list the different types of human genomic variation, explain the mechanism by which each occurs, and discuss the consequences of the variations. Where appropriate, specific examples of human disorders are related to the variations. Topics include chromosomal, biochemical, and DNA sequence variation; mitochondrial genome variation; and epigenetic effects.

For the purpose of this course, health informatics is defined as the convergence of information technology, information management, and health care, at various levels, ranging from simple data gathering to the design and implementation of new health care information systems. This course helps students understand the role of information systems and technology within a health care organization. It examines the business and technical issues associated with the selection, deployment, and use of health informatics – both in clinical and back-office areas.

This course provides students with a sound understanding of the fundamental concepts and methods for conducting public health research. After a brief introduction to the philosophy of science, the major emphasis in the early portion of the course is on research conceptualization, design, and measurement, with a particular focus on the logic of minimizing rival alternative explanations of finding for experimental and quasi-experimental studies.

This course covers statistical methods for analysis and interpretation of survival data arising from clinical trials. Topics include survival curves, estimation of sample size, survival curves, proportional-hazard models, time-dependent variables, and prognostic indices.

This course focuses on application of high-throughput genomic technologies in a variety of biological contexts. The expectation is for students to achieve the following course learning objectives:

• Gain familiarity with the technologies and techniques available for high throughput genomic studies;
• Understand the data outputs and how to store/manipulate/analyze/interpret results;
• Work with high performance computing in the area of genomic analysis;
• Become aware and be able to use tools for genomic data storage, annotation, and curation;
• Critically plan and perform quality control of analysis pipelines and results;
• Share results of genomic data through communication and visualization techniques.

Prerequisites: Familiarity with basic genetics, genome structure, and the methods and approaches of molecular biology. Please ask for primers on these topics if you do not have a biology background. Additionally, students must have basic knowledge in the Unix environment and basic operations. Materials are distributed in the first week of class to address.

This three-credit course aims to familiarize students with the basic use of SAS and R for routine statistical analysis and prepare them for more advanced courses and/or thesis research. Statistical computation is illustrated with examples in medical research, biological study, and business. The course focuses on the computing environment and does not provide a thorough discussion of statistical theories. It is expected that students will already be prepared statistically.

This first part of this course is an advanced treatment of the key ideas undergirding the design and analysis of contemporary clinical trials. By the end of the course, students will understand the statistical foundations of Phase I, II, and III trials from the standpoint of classical Frequentist, Bayesian, and adaptive designs. In addition, students will learn the usual mechanisms for preparing a clinical trial protocol, data safety and monitoring, interim analysis, and proper close out of a trial.

This course covers an introduction to probability and statistics including descriptive statistics, tests of hypothesis, regression analysis, contingency tables, nonparametric tests, and life tables. Students gain hands-on experience in the analysis of medical data using several computer systems and at least one of the different statistical packages such as BMDP, SAS, PSTAT, SYSTAT, and Minitab.

This course is a continuation and elaboration of EPH601. Topics include the design of factorial experiments, analysis of variance and variance components, multiple linear regression, and life tables.

This course covers advanced statistical methods used in analyzing data from epidemiologic investigations. Topics include Mantel-Haenszel chi-square, interaction, standardization of rates, incidence density, logistics regression, and other topics.

This seminar examines current global public health issues, governance, and decision-making challenges for the 21st Century across developing, transitioning, and developed countries. Topics of discussion include new actors for world health in the era of globalization; linking human development, poverty and health inequities; social, cultural, and ethical considerations for health planning; the role of industry, trade, and public health; evidence-based research for improved global health initiatives; foreign policy and health security challenges associated with emergence and re-emergence of infectious diseases and public and private partnerships in global health.

This course introduces students to the science of prevention and health promotion. More specifically, through didactic presentations, group discussions, article readings, and critiques, and a term project, this course will focus on providing students with an overview of the top preventable causes of disease in the U.S., the etiology of disease (with a focus on the top preventable causes of disease in the U.S.) across the lifespan, the role of prevention theories in the development of preventive interventions, and the role of methodology in prevention science. The course will also provide an overview of efficacious/effective preventive interventions, including (but not limited to) family community, and school-level interventions. Examples from the fields of obesity, drug use, smoking, and HIV will be used to illustrate the course learning objectives detailed below.

This course provides an introduction to the multiple systems that define, describe, and shape the delivery of healthcare in the United States. Using case studies and presentations of major issues, this course will give the learner an appreciation of the dilemma confronting policymakers, providers, and patients: how to balance cost, quality, and access. 

Community-based participatory research (CBPR) is methodology, increasingly popular in public health and other disciplines, which invites community collaboration throughout the research process from conceptualization of study focus to dissemination of findings. This course will provide an opportunity for medical and graduate students to better understand the process by which community members and academic researchers work collectively to address health disparities and influence social change.