METHODOLOGIC CHALLENGES IN EPIDEMIOLOGIC RESEARCH Syllabus
Course Learning Objectives
Upon successfully completing this course, students will be able to:
- Identify complex methodologic problems in epidemiologic research, such as (a) missing data, (b) information bias, (c) confounding bias, (d) selection bias, (e) multiple exposures, and (f) multilevel determinants of disease, and state implications for etiologic inference
- Apply appropriate analytic tool(s) (e.g. , multiple imputation, propensity scores, inverse probability weighting, regression calibration, and multilevel models) to diagnose and account for complex methodologic problems, such as those listed above
- Evaluate the sensitivity of an etiologic inference to possible bias due to complex methodologic problems, such as those listed above
Integrates and extends material learned in the three-course Epidemiologic Methods sequence. Focuses on the application of strategies for addressing key methodologic challenges that arise when carrying out epidemiologic research. Incorporates experiential learning components, including computer-based laboratory exercises and a practicum, which require working knowledge of the free statistical package R.
Intended AudienceEpidemiology doctoral and other advanced students.
Methods of Assessment
40% multiple-choice or short answer examination, 40% practicum (structured poster presentation), 20% participation in lectures and laboratories.
Additional Faculty Notes:
Late assignment policy:
Course grades are based on the final exam, the final practicum poster performance, and on lab attendance. Absence from the final exam or practicum presentation is not allowed except in extraordinary circumstances requiring advance instructor permission.
PrerequisitesEpidemiologic Methods 1-3 (340.751 – 340.753) and either Statistical Methods in Public Health I-III (140.621 – 140.623) or Methods in Biostatistics I-III (140.651 – 140.653).
Additional Faculty Notes:
There are no required textbooks for this course. All required (and most recommended) readings will be available on-line in CoursePlus. Interested students may wish to consider acquiring a standard epidemiology textbook such as those listed below:
Rothman, K.J., Greenland, S. and Lash, T.L. (2008). Modern Epidemiology. Philadelphia, PA: Lippincott Williams & Wilkins.
Savitz, D.A. (2003). Interpreting Epidemiologic Evidence: Strategies for Study Design & Analysis New York: Oxford University Press.
Please see the course Session for a full list of dates and items for this course.
Academic Ethics Code
Students enrolled in the Bloomberg School of Public Health of The Johns Hopkins University assume an obligation to conduct themselves in a manner appropriate to the University's mission as an institution of higher education. A student is obligated to refrain from acts which he or she knows, or under the circumstances has reason to know, impair the academic integrity of the University. Violations of academic integrity include, but are not limited to: cheating; plagiarism; knowingly furnishing false information to any agent of the University for inclusion in the academic record; violation of the rights and welfare of animal or human subjects in research; and misconduct as a member of either School or University committees or recognized groups or organizations.
1. Identify complex methodologic problems in epidemiologic research, such as:
(a) missing data,
(b) information bias,
(c) confounding bias,
(d) selection bias,
(e) inferential challenges in repeated measures nested within persons,
(f) inferential challenges in persons nested within places.
and state implications for etiologic inference;
2. Apply appropriate analytic tool(s) (e.g., multiple imputation, propensity scores, inverse probability weighting, regression calibration, and multilevel models) to diagnose and account for complex methodologic problems, such as those listed above; and
3. Evaluate the sensitivity of an etiologic inference to possible bias due to complex methodologic problems, such as those listed above.
General organization of the course:
The course consists of a series of lectures, practicum sessions, and laboratory exercise sessions. Lectures will be held from 8:30 a.m. to 9:50 a.m. on Mondays, Wednesdays and Fridays. Practicum sessions and labs will be held from 10:00 a.m. to 11:50 a.m. on Mondays and Wednesdays in a separate classroom from lectures. The schedule of lectures, practicum sessions, and labs, as well as the reading list, are attached.
Lecture slides will be made available on CoursePlus in the form of PDF documents. Additionally, each lecture will be recorded and posted to the CoursePlus website in .mp3 format after the lecture.
Lab exercises are undertaken in groups of 6 students. Students will choose their lab group for this course. Be prepared to form lab tables on the first day of class. Questions about lab groups can be addressed to Ayesha Khan, the Course Coordinator, in Room W6508. Students will be expected to sit at the same table for the entire term. Attendance at all laboratory sessions is required and will be recorded. Lab exercises will be distributed in class on the Friday before. Students are expected to complete the lab prior to the lab discussion session the following Wednesday.
Exercises will require the use of course datasets and R statistical software; students should bring their laptop computers to class for use during lab and practicum sessions. Students may also conduct supplemental analyses using alternative software packages (such as STATA or SAS) but will be required to use R to execute data management and analyses for labs. For those students who are new to R, we emphasize that, like any new language, learning R will be challenging and will require persistence. We will provide you with additional tools to help you get acclimated. Students are encouraged to seek out additional resources online or through other courses.
The practicum portion of the course will be completed in self-selected subgroups of 3 at each lab table. During specifically designated practicum sessions (see schedule below), faculty and TAs will offer mentoring on the development and analysis of a student-chosen research project using course datasets. Details related to the practicum will be available in a separate document (Practicum description.doc) on the CoursePlus website. The culmination of the practicum will involve a presentation by each 3-person group of a final poster on May 14 2014 in Feinstone Hall. Attendance at all practicum sessions as well as the formal poster presentation session is required.
Students can access the web supplement for this course through the CoursePlus system. You must create an eLearning account to access the CoursePlus website for this course. The “online library” of the course website contains audio recordings and printable handouts of each lecture. Course schedules, announcements, relevant links, and other organizational information are posted regularly on the course website.
Peer instruction using Poll Everywhere
Since the original design of this course, we have emphasized discourse between students, and between faculty and students, as a major learning tool. In an effort to facilitate this interaction, selected lecture sessions will include the use of instant feedback and discussion using Poll Everywhere, an online tool that allows students to answer questions using mobile devices (text messaging, instant messaging), or laptops (online access) in real time. Results will be shown in class and will serve as the basis for one-on-one discussion among students during class and to track aggregate student performance across the term. Friday sessions will generally involve discussion of all material of the week including concepts and tools lectures as well as the lab. Students are encouraged to submit written questions by Thursday so that they may be included as polleverywhere questions for the Friday session.
If you have a question about the course administration (i.e., scheduling a makeup exam), please contact the course coordinator Ayesha Khan directly (e-mail: firstname.lastname@example.org). For questions related to course content, we recommend that you ask the Instructors and Teaching Assistants in lectures, lab, or office hours.
TA office hours
The course teaching assistants will hold office hours on Thursdays from 3:00-5:00 p.m. to answer questions about the course content (location to be announced). A schedule of TA office hours is found on the CoursePlus website. Additional TA office hours are scheduled on the days leading up to the exam.
This is intended as a doctoral-level course for students interested in a research career. A doctoral-level course should emphasize the development of the capacity for critical scholarly inquiry and discourse, not just knowledge acquisition. The process of learning is as important as the content. A core feature of this course will be the expectation that students engage in a collaborative and interactive process of questioning and exploration with each other and with the instructors. Given the advanced nature of this course, unambiguously right or wrong answers are often not available; there will always be better or worse answers. The instructors will provide feedback about the answers to lab and exam questions, but no “answer key” will be distributed.
The instructors assume adherence to the academic ethics code, as discussed in the Policy and Procedures Memorandum for students, March 31, 2002.
Disability Support Services
If you are a student with a documented disability who requires an academic accommodation, please contact Betty H. Addison in the Office of Career Services and Disability Support (email@example.com, 410-955-3034, Room E1140).
Alison Abraham, Ph.D.
Thomas A. Glass
Disability Support ServicesIf you are a student with a documented disability who requires an academic accommodation, please contact the Office of Student Life Services at 410-955-3034 or via email at firstname.lastname@example.org.