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At SER 2019, the Cassel lecture was delivered by Miguel Hernán and Sandro Galea on the topic of  reconciling social epidemiology and causal inference. Their talk was turned into a paper in the American Journal of Epidemiology, and in March 2020, was published along with a series of responses by Drs. Enrique Schisterman, Whitney Robinson and Zinzi Bailey, Tyler VanderWeele, and John Jackson and Onyebuchi Arah.  In this SERious Epi bonus journal club episode, we had conversation with Dr. John Jackson and Dr. Onyebuchi Arah about their commentary and had the opportunity to ask their thoughts on the other topics published in that issue.

Links:

• Win-Win: Reconciling Social Epidemiology and Causal Inference by Sandro Galea and Miguel A Hernán
  https://doi.org/10.1093/aje/kwz158
• Editorial: Let’s Be Causally Social by Enrique F Schisterman
  https://doi.org/10.1093/aje/kwz237
• Invited Commentary: What Social Epidemiology Brings to the Table—Reconciling Social Epidemiology and Causal Inference by Whitney R Robinson, Zinzi D Bailey
  https://doi.org/10.1093/aje/kwz197
• Invited Commentary: Counterfactuals in Social Epidemiology—Thinking Outside of “the Box”
  https://doi.org/10.1093/aje/kwz198
• Invited Commentary: Making Causal Inference More Social and (Social) Epidemiology More Causal by John W Jackson and Onyebuchi A Arah
  https://doi.org/10.1093/aje/kwz199
• Galea and Hernán Respond to “Brings to the Table,” “Differential Measurement Error,” and “Causal Inference in Social Epidemiology”
  https://doi.org/10.1093/aje/kwz201

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Have you ever wondered why it is so important to consider the concept of time in epidemiologic analyses? And, more importantly, what strategies exist to appropriately account for time and time-varying variables? Time dependent confounding? In the first-ever episode of SERious Epidemiology, Dr. Eleanor Murray will be discussing the concept of time in epidemiologic research and explaining different types of time-related bias.

After listening to this podcast, if you’re interested in learning more about time or checking out any of the resources mentioned on this podcast, links are included below:

1. Young, J.G., Vatsa, R., Murray, E.J. et al. Interval-cohort designs and bias in the estimation of per-protocol effects: a simulation study. Trials 20, 552 (2019).

https://trialsjournal.biomedcentral.com/articles/10.1186/s13063-019-3577-z

2. Weuve J, Tchetgen Tchetgen EJ, Glymour MM, et al. Accounting for bias due to selective attrition: the example of smoking and cognitive decline. Epidemiology. 2012;23(1):119-128.

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3237815/

3. Hernán MA, Robins JM (2020). Causal Inference: What If. Boca Raton: Chapman & Hall/CRC.

https://www.hsph.harvard.edu/miguel-hernan/causal-inference-book/

4. Society for Epidemiologic Research 2019 Annual Meeting Symposium Presentation

“The Baddest of the Bad: Ranking the Most Pernicious Biases Facing Observational Studies”

Catherine Lesko, Matthew Fox, Robert Platt, Maria Glymour, Jessie Edwards, Ashley Naimi, Chanelle Howe, Jay Kaufman

https://epiresearch.org/2019/06/21/the-baddest-of-the-bad-ranking-the-most-pernicious-biases-facing-observational-studies/

For anyone interested in learning more specifically about immortal time bias, this paper is a terrific introduction:

Lévesque LE, Hanley JA, Kezouh A, Suissa S. Problem of immortal time bias in cohort studies: example using statins for preventing progression of diabetes. BMJ. 2010;340:b5087.

https://www.bmj.com/content/340/bmj.b5087.long

Podcast: Play in new window | Download (Duration: 10:39 — 14.6MB)

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Do you want to know more about novel methods in epidemiology but don’t have the time read a bunch of papers on the topic? Do you want to keep current on the latest developments but can’t go back to school for another degree? Do you just want the big picture understanding so you can follow along? SERious EPI is a new podcast from the Society for Epidemiologic Research hosted by Hailey Banack and Matt Fox. The podcast will include interviews with leading epidemiology researcher who are experts on cutting edge and novel methods. Interviews will focus on why these methods are so important, what problems they solve, and how they are currently being used. The podcast is targeted towards current students as well as practicing epidemiologists who want to learn more from experts in the field.