Note: Some courses run the entire semester (example: Fall 1 and 2), others run only part of the semester (example: Spring 2), and some courses last only a few weeks. Review course details below and read the syllabus for more information.
Fall Elective Courses
This course provides a comprehensive introduction to randomized controlled clinical trials. Topics include types of clinical trials research (efficacy and effectiveness trials), study design, treatment allocation, randomization and stratification, quality control, analysis, sample size requirements, patient consent, data safety and monitoring plans, reporting standards, and interpretation of results. Course activities: lectures, manuscript critiques, class project, and paper.
This course will introduce students to the most commonly used qualitative methods for health-related research and implementation science. It will provide a foundation in the application of qualitative methods to medical and health research. Topics addressed will include uses of qualitative data, designing studies, sampling strategies, collecting data, and qualitative analysis. A variety of methods will be discussed, with an emphasis on using focus groups and various interviewing techniques. Using case-based examples from active research studies, students will learn the best practices in qualitative research, how to plan and critically evaluate qualitative studies and articles, and fundamentals of writing strong qualitative aims for grant proposals. Upon completion of the course, students will be able to plan, propose, conduct, and analyze a qualitative study. Course activities are primarily discussion-based and include: case-based presentations, literature critique, and study development. All deliverables are purposefully designed to help a student walk through the steps of planning, proposing, and conducting qualitative research. Guest lectures by trained qualitative methodologists who are active qualitative researchers are included.
In this course, we will introduce students to the methods and applications of decision analysis and cost-effectiveness analysis in health care technology assessment, medical decision making, and health resource allocation. At the conclusion of the class, the student will have an understanding of the theoretical basis for economic evaluation and decision analysis, its application, and hands-on experience in the application of the methods. Among the topics covered are the development of a research question, choice of decision perspective, development of a decision analytic model, estimation of costs and benefits, use of preference based measures, addressing uncertainty and preparation of a manuscript presenting a decision analytic study.
The purpose of this course is to explore how structural and social determinants of health (SSDoH) produce and maintain health disparities. There will be a variety of learning modalities, including expert guest lectures to discuss cutting-edge research, key foundational and recent readings related to SSDoH and health disparities, and in-class discussion. The course will use case studies and a research proposal to help students apply what they’ve learned to real-life situations. By the end of the course, students will be able to (a) define health disparities; (b) explain how social and structural determinants of health – including interpersonal and structural racism – produce and maintain health disparities across each phase of disease development; and (c) identify strategies for assessing and addressing health disparities in their own research.
Winter elective course
A critical step in the dissemination of population-level clinical research is communicating research findings and key messages to the media and lay audiences. With conflicting messages coming from advocacy groups and others, the burden falls on the clinician-researcher to distill complex information, dispel misinformation, and tell a compelling story that resonates with the audience. The course will equip students with the skills, technique, experience and confidence needed to give successful, engaging media interviews and presentations related to the publication of research and expertise-specific topics. Through critique, tape and review exercises, class discussion, and guest speakers, students will learn all the facets that make an interview or presentation successful, including nonverbal communication and delivery skills (body language and vocal interpretation), content and messaging, and navigating interactions with the media. The instructor will evaluate each student’s skill set, on which the student will build throughout the course in a series of on-camera experiences.
Spring elective courses
This course will provide a comprehensive introduction to principles of shared decision making and health literacy and their implications for clinical communication. Topics may include basic and applied research on shared decision making, principles of designing and evaluating patient decision aids, principles of health literacy, research on relationship between health literacy, numeracy, and health outcomes, best practices for communication with low-numerate and low-literate individuals, best practices (and controversies) in communicating probabilities and their associated uncertainty about screening and treatment outcomes, and best practices for designing and evaluating written information for clinical populations (such as intake forms, brochures, and informed consent documents).
Course activities: lectures, manuscript critiques, class project, paper
Introduction to the use of meta-analysis and related methods used to synthesize and evaluate epidemiological and clinical research in public health and clinical medicine. Concepts introduced and illustrated through case studies of public health and medical issues. Course activities: lectures, class discussion, group project, and paper.
Course note: M21-570 or equivalent required prerequisite.
This course will provide the knowledge and principles of predictive modeling, with applications to clinical and population health settings. Topics covered will include design, conduct, and application of risk predictions; statistical methods and analysis for model development and validation; evaluation of prediction models; emerging new methods; and risk stratification to identify a risk group, to assess eligibility to clinical trials and interventions, and to guide prevention priorities. The student will learn these topics through lecture, class discussions, data analysis lab, and homework.
Course note: Biostatistics I and II (M21-560 and M21-570) or equivalent required prerequisite.
The objective of this advanced graduate course is to prepare students to understand and use large administrative healthcare databases to perform epidemiologic / health services research on the utilization and comparative effectiveness of healthcare services. Lectures will cover the translation of clinical care into healthcare utilization data, review various types of national and state administrative databases, describe methods for administrative database research, and emphasize key issues related to data security and confidentiality. We will consider the strengths and limitations of observational studies using large databases to augment evidence from randomized clinical trials. Students will develop a research proposal in their own area of interest and complete a small research project that uses administrative data; this project will require programming with R statistical software to identify study populations and perform analyses; interpreting results; and presenting to the class. Students will further gain experience with healthcare database research by reviewing journal articles weekly.
Course note: Students must contact the instructor two weeks prior to the start of class to discuss ideas for the class project. M19-501 Introductory Clinical Epidemiology and M19-511 Introductory Biostatistics for Clinical Research are required prerequisites. R software required. Students may download freely available R Studio software on their laptop or desktop computer.
This course provides an overview to dissemination and implementation (D&I) science (i.e., translational research in health). Topics include the importance and language of D&I science; designs, methods and measures; differences and similarities across clinical, public health and policy settings; selected tools for D&I research and practice; and future issues. Course activities: Lectures, class discussions, manuscript critiques, and class project (culminating in a poster).
The purpose of the Independent Study course is to develop and refine the skills students learn in the fall core courses, Introductory and Intermediate Clinical and Epidemiology and Biostatistics series. Students enrolling in this course must come prepared with a circumscribed and well-defined project that relates to public health and population sciences. A research mentor within Washington University School of Medicine must be identified and approved of by MPHS leadership prior to the course enrollment. Objectives, a synopsis and milestones of the project per each student’s individualized syllabus should be identified and submitted to the MPHS leadership and mentor prior to the start of the semester. Students will be expected to submit a report, for example, drafted manuscript, an abstract for a conference, data analysis results, at the end of the spring semester to the MPHS leadership for credit. Course credit will be evaluated by both the research mentor and MPHS leadership. This two-credit course will be offered only as a pass/fail course to current MPHS students.
Course note: Prerequisites include approval from MPHS leadership and students must have completed the Introductory and Intermediate Clinical and Epidemiology and Biostatistics series. The course will meet on the first and last Friday of the Spring semester at 1-3pm, and students will present their independent study project. Also, A mid-term progress report is required. The Approval Form must be completed and approved by Dr. Yikyung Park prior to class registration.
M19-610 Multilevel and Longitudinal Data Analysis for Clinical and Public Health Research
Spring 1 and 2; Tuesdays 9 am – 12 pm
Y. Yan, MD, PhD
This course is designed for medical students, clinicians, epidemiologists, and other public health researchers.
The topics include basic statistical concepts and methods for continuous, categorical, count, and time-to-event data in multilevel and longitudinal settings. Through lectures, SAS lab, homework assignments, and a small project, students will understand the basic statistical concepts and methods for these types of data, will be able to address research questions using the concepts and methods, will be able to perform basic data analyses with SAS software, will be able to interpret the results in the context of clinical/epidemiological/public health research.
Course prerequisite: (1) M19-512 or knowledge of generalized linear model and Cox PHM. (2) introductory knowledge of SAS, and (3) availability of SAS software for the class.