Announcing May BEMC Talk

Dear Berlin-area Epidemiological Methods Enthusiasts,

You are invited to our next BEMC Talk on Wednesday, May 8th.

BEMC Talk: Wednesday, May 8th, 2019 @ 4pm

“Machine learning for population-based health studies” – Christoph Lippert, Potsdam

  • Please register online:
    • If the registration link does not work for you please send us an email at bemcolloquium(at) with your full name and email address and short note that you’d like to be added to the registration list. Thanks!
  • Description: “In my talk, I’ll introduce our research at the interface of machine learning and statistics towards new methods for large-scale epidemiologic studies. We are working on models and algorithms that allow us to analyze high-dimensional genotypes and phenotypes from imaging and sequencing at scale. I’ll be talking about our work on mixed models for confounder correction, quantitative phenotyping using deep learning, as well as our works on novel statistical tests on deep learning embeddings of images.”
  • Location: Seminar room of the Neurology Clinic; Bonhoefferweg 3 entrance, 3rdfloor, Charité – Campus Mitte

Upcoming Berlin Epi Events:

  • May 15th – BEMC JClub – Paper posted online
  • June 5th – BEMC Talk – “Cool applications in R for epidemiologists” – Jochen Kruppa, Berlin
  • June 19th – BEMC JClub – Paper will be posted online
  • July 3rd – BEMC Talk Suzanne Cannigieter, Leiden, Netherlands
  • July 19th – BEMC JClub – Paper will be posted online in late May
  • August 2019 – no BEMC Talk or JClub
  • September 19th – BEMC JClub – Paper will be posted online in late July
  • October 2nd – BEMC JClub – Paper will be posted online in late August
  • Wednesday, October 23rd – IPH Lecture Partner event– John Gill from Vancouver

Interested in other Institute of Public Health events? Visit our calendar to check out upcoming conferences & short courses!

Follow BEMC on Twitter and leave questions for our speakers: @BEMColloquium

Pamela Rist, “One Size Does Not Fit All: Teaching Introductory Epidemiology” — Student summary

Dear BEMCers,

We are pleased to be able to share a summary prepared by three students who are currently earning credit for participating in the BEMC. A warm thank you to the three anonymous students for letting us share a compilation of their summaries!

As a reminder, we will be having BEMC JClub next Wednesday. A link to the article is available under JCLUB.

On Wednesday, April 3rd, Pamela Rist, Assistant Professor of Medicine at the Harvard Medical School and Assistant Professor of Epidemiology at the Harvard T.H. Chan School of Public Health, held a lecture about different ways to teach introductory courses in epidemiology. She teaches intro epi classes for medical staff and public health professionals, at undergraduate, postgraduate and doctoral levels.

Rist started her lecture by presenting how she first introduces the concept of confounding in her classes. Touching only the most basic aspects, the concept is simple to understand. However, it becomes inaccurate when moving on to more complex causal relations: It does not consider (open) backdoor paths, for instance. This, according to Rist, illustrates the difficulty of teaching: One has to introduce topics in a way that is understandable to the audience but does not cause problems later on for those who progress to substantial classes. Ultimately, there can be a trade-off between an intuitive and a correct definition.

She further explained that what should be thought of in an epidemiology introductory course depends on the type of audience, the skill set they need to learn in their career tracks, the goals of the course and structural issues. This appears to be particularly challenging since these courses are usually characterized by a considerable audience heterogeneity: students have different backgrounds, different coursework and different degrees of interest.

A successful technique to capture an audience’s attention, and to let students understand the importance of the epidemiology in their own field, might be to use different examples and papers based on the audience’s field of interest. In addition, Rist stated that even the way epidemiological concepts are explained should be flexible: using not only medical but also general knowledge examples to explain complex concepts (she provided a striking example explaining the Selection Bias using NBA players as subject).

Lastly, Rist introduced different teaching forms she uses: seminars, online lectures, inverted classrooms and discussed possible advantages as well as drawbacks. Seminars are a good learning environment, however small groups are required for discussion. Therefore, several rooms need to be available as well as teaching assistants to lead the seminars. If the setting does not allow this, Rist advised including live polls in large lectures and polling twice: The first time the students answer the question alone, the second time after discussion with neighbors. In this way, an active element can be incorporated into the lectures and students can learn from one-another. Rist also gave voice to her concern that setting up online lectures for the first time requires intensive preparation.

Q: Where do you find the papers for the discussion with students?
A: Science and Health sections of Times, JAMA, NEJM, Lancet. Talking to family and friends about current issues.
Q: Do you discuss good or bad papers?
A: I try not to focus only on poorly written papers. Sometimes I use also older papers to discuss the constraints of older methodology.
Q: What role does the student evaluation play for your next course?
A: Our course evaluations are done externally and anonymously, but I do read them. I find them to be useful and definitely try to adjust based on feedback where possible.
Q: How do you measure the success of your teaching? Do you have a special format for the exams (multiple choice etc.)?
A: Formally, the school measures the success of the lecture on 1-5 scale (teachers must score at least 3.5 to be allowed to teach the following semester). Informally, by student reactions. In terms of exams: we use tests with multiple choice, true/false, and fill in the blank questions for Master’s students (the class size is quite large and there isn’t much time between the final exam and when grades are due) and essays for PhD students.