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:

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

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/

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

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

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