This is the second course in a two-course series P9419-P9420 required of all candidates for the M.P.H. and M.S. in epidemiology. This course focuses on the Statistical Analysis, Results and Discussion sections of students' master's theses. Students will work closely with their first and second readers during the semester, but course instructors and teaching assistants will provide guidance on the selection and conduct of statistical analyses, and on transforming their thesis into a format appropriate for submission for publication.
This course will introduce students to the theoretical and practical aspects of applying a “causal roadmap” to research questions in epidemiology using both single timepoint and longitudinal data. A causal roadmap approach to empirical investigation is intended to strengthen transparency and clarity in the research process and typically consists of several steps including: 1) formulating a research question, 2) translating it into a causal quantity, 3) listing the assumptions required to identify this causal quantity from the data, 4) choosing an estimation approach, and 5) doing the analysis. We will learn single timepoint and longitudinal g-computation/ standardization, inverse-probability-of-treatment weighting (IPTW), and doubly robust estimation approaches (e.g., targeted minimum loss-based estimation (TMLE)). The final class will include integrating machine learning into the estimation approach. Each module will include hands-on exercises in R in which we will apply the estimation approaches to data. Data for each analysis exercise will be provided by the instructor. For the final project, students can choose to use data provided by the instructor or data for which they already have access.
This course provides instruction in the preparation of grant applications for the National Institute of Health (NIH) or other granting agencies, with a special emphasis on F31 and R36 grant applications. Students will participate in instructional lectures, learning the foundations of grant writing and how to craft the specific sections of an NIH-style proposal, and discussions. This course is intended for all PhD students in Epidemiology, as it helps them with grant application experience This course is also intended to provide a forum for 2nd year PhD students to begin to formulate and develop a research question that becomes the basis for their Foundation Essay, dissertation, and a proposal they can submit for NIH funding.
Primarily for fellows in the Psychiatric Epidemiology Training Program. Presentation and discussion of ongoing faculty and fellow research, plus guest speakers. Designed to provide constructive criticism of research in progress and to make fellows aware of current issues in psychiatric epidemiology.
The focus of this class will be on providing students with the knowledge, experience, and resources needed to select and apply advanced epidemiologic techniques. Core techniques have been selected based on their current and potential future use in the field of epidemiology.The course will be organized into four modules, each organized around a specific technique in relation to available alternatives for (1) working with missing data, (2) tackling non-linear trends, (3) placing non-independent observations in context, and (4) strengthening causal inference from observational data. This course is limited to Epi doctoral students only.
Independent research with individual faculty. Tailored to the particular interests and needs of the individual student. May include literature review, research projects, or other special studies that enrich the student’s program.
Independent research with individual faculty. Tailored to the particular interests and needs of the individual student. May include literature review, research projects, or other special studies that enrich the student’s program.
Neurological disease epidemiology is the study of the distribution and determinants of these diseases in human populations; it poses a set of novel challenges given the complex nature of the underlying organ. What are these challenges and what issues set this branch of epidemiology apart from others? The purpose of this class is to introduce students to the core principles of neuroepidemiology. The class will be strongly grounded in clinical neurology. Highlighted are a number of diverse disorders, including Alzheimer's disease, cerebrovascular disease, Parkinson's disease, essential tremor and epilepsy, many of which are exclusive to humans. The epidemiology of these disorders will be the focus of a series of lectures. During the semester, we will also explore disease clusters, socio-medical aspects of these diseases, and interventional studies that attempt to alter their course.
This elective course in the Department of Epidemiology is intended for MS and MPH students. In the past, we have had a mixed audience of graduate students in epidemiology and other departments in the Mailman School of Public Health as well as medical students and physicians who are pursuing epidemiological training. This blend of students leads to a rich and varied discussion. Our overarching goal is to open a world for students, expose them to a new body of knowledge, and get them to think about a series of thorny epidemiological issues. More specifically, students will gain a thorough understanding of the normal and abnormal workings of the brain and be able to identify and explain how the clinical expression of the latter creates an interesting and often distinctive set of challenges for epidemiologists as they attempt to screen for, diagnose and study the determinants of these uniquely human diseases.
This course is organized as a writing seminar/workshop focused on practical writing and oral presentation skills. Students will identify for themselves an empirically-based manuscript or in-depth literature review on which they will work throughout the semester. Specific portions of the writing project will be completed on a regular basis and will be reviewed and critiqued by fellow students and the instructor. Students also will write a draft Specific Aims for a research proposal related to their manuscript topic and make an oral presentation based on the manuscript. Didactic presentations and discussions will focus on the structure of manuscripts, presentations, and grants; writing and presentation challenges and strategies to address them; and other aspects of manuscript preparation (e.g., choosing a journal). Limited to 1st year Epi Doctoral students.
Students in the Biological Science PhD program only. Independent research in approved thesis sponsor laboratories.
Doctoral candidates are required to make an original investigation of a problem in biomedical engineering, the results of which are presented in the dissertation.
Open only to certified candidates for the Ph.D. and Eng.Sc.D. degrees. Doctoral candidates in chemical engineering are required to make an original investigation of a problem in chemical engineering or applied chemistry, the results of which are presented in their dissertations. No more than 15 points of credit toward the degree may be granted when the dissertation is accepted by the department.
All doctoral students are required to attend the department seminar as long as they are in residence. No degree credit is granted.
Open to Executive MPA Only.
This is a course during which the mid-career executives who are enrolled as students in the Executive MPA program exhibit and share professional work they have managed or directly created during their first year in the program. Materials are presented to the faculty and students for criticism, analysis, and potential improvement.
All doctoral students are required to complete successfully four semesters of the mechanical engineering seminar MECE E9500.
Open only to microbiology students. Students doing dissertation research register for this course, as well as students who are rotating through laboratories of staff members.
Using the format of a research seminar highlighting research “challenges” of the DNSc faculty , this course is designed to strengthen the student’s ability to integrate and synthesize knowledge in statistics and nursing research methodologies, and to apply this integrated knowledge to common problems in study design and data analysis.