In this episode of Season 2 of SERious Epidemiology, Hailey and Matt go back to Chapters 2 and 3 of Modern Epidemiology but this time with guest Dr. Jay Kaufman of McGill University. We focused on the causal inference revolution and how our thinking on some of the issues in the chapter have changed over time as we learn more about these topics.
Category Archives: Episodes
In this episode of Season 2 of SERious Epidemiology, Hailey and Matt try to finish off Chapter 3 of Modern Epidemiology given they couldn’t get it all into one episode as originally promised. We talked about potential outcomes, sufficient causes models and DAGs (very hard to do in audio only). We focus on the assumptions for causal inference. And we make a pitch for a Modern Epidemiology Audio Book…read by James Earl Jones.
In this episode of Season 2 of SERious Epidemiology, Hailey and Matt take on Chapters 2 and 3 of Modern Epidemiology… at least that was the plan, we really only got to chapter 2 so we’ll be back again in our next episode for Chapter 3. But in this episode we focused on some key insights around replicability and reproducibility. And camp color wars. You’ll have to listen to understand.
We are going in a new direction for Season 2 of SERious Epidemiology. This season Hailey and Matt are focusing exclusively on the new fourth edition of the textbook Modern Epidemiology. The textbook has played such an important role in the training of epidemiologists since the first edition was released and has taken on an even larger role within the field as more editions have come out. We will work through each chapter and talk about what key insights we got from them and we will talk to guests about their experiences with the text. In this first episode of the season, we are delighted to present our interview with Dr. Kenneth Rothman, author of the first edition and co-author of editions two through four.
Link to Modern Epidemiology:
Link to Epidemiology: An Introduction
Join Matt Fox and Hailey Banack for our final episode of the first season of SERious Epidemiology, a season which happened to take place entirely during the COVID-19 pandemic. The pandemic has raised countless public health issues for us all to consider from virus testing to health disparities to safe classrooms to vaccine distribution. For the first time (maybe ever), nearly everyone knows what epidemiology is, and we are all hopefully done with having to explain that we are not a group of skin doctors (“we study epidemics… not the epidermis”). In this episode we discuss a few pandemic-related issues particularly relevant for epidemiologists, such as whether we’ll ever have to wear work pants again, the use pre-prints and the value of peer review, and issues related to confirmation bias.
In this journal club episode, Dr. Matt Fox and Dr. Hailey Banack discuss a paper recently published in the New England Journal of Medicine by Dagan et al. on the Pfizer COVID-19 vaccine. Listen in for a real-world example of the concept of emulating a target trial and a discussion of how an epidemiologic study can be described as truly beautiful.
Dagan N, Barda N, Kepten E, Miron O, Perchik S, Katz MA, Hernán MA, Lipsitch M, Reis B, Balicer RD. BNT162b2 mRNA Covid-19 Vaccine in a Nationwide Mass Vaccination Setting. N Engl J Med. 2021 Feb 24:NEJMoa2101765. doi: 10.1056/NEJMoa2101765. Epub ahead of print. PMID: 33626250; PMCID: PMC7944975.
The topic of this episode is lifecourse epidemiology, defined by Dr. Paola Gilsanz as the biological, behavioural and social processes that influence an individual’s health outcomes throughout their life. Join us as we discuss models commonly used in lifecourse epidemiology, such as the early life critical period model, accumulation model, and pathway model. Is lifecourse epidemiology different than social epidemiology? Is all epidemiology lifecourse epidemiology because we study individuals at some point in their lifetime? Dr. Gilsanz answers these questions for us and also highlights the importance of using different data sources depending on your question of interest and the specific types of bias that are particularly prevalent in lifecourse epidemiology.
Brazilian cheese bread recipe:
Ask yourself these true or false questions:
- Generalizability and transportability and external validity are all the same thing
- Generalizability is a secondary concern to internal validity
- We spend too much time in epi training programs teaching internal validity and not enough teaching external validity
- Worrying about external validity is largely and academic exercise that doesn’t really have much in the way of real-world impact.
In this episode of SERious Epi we discuss these questions and more with Dr. Megha Mehrotra. While internal and external validity are familiar to nearly all epidemiologists, the concept of transportability is less familiar. Listen in to this episode for a clear description of how concepts related to validity, generalizability, and transportability are similar, and different, from each other.
Matching is something we learn about in our intro to epidemiology classes and yet we probably spend little time thinking about it after that, we just do it. But when should we match and when does it help us and when does it hurt us? What do we need to consider before we match? Dr. Anusha Vable joins us to help us understand matching in detail.
For those of you looking to do more reading around matching see:
- Ho, D., Imai, K., King, G., & Stuart, E. (2007). Matching as Nonparametric Preprocessing for Reducing Model Dependence in Parametric Causal Inference. Political Analysis, 15(3), 199-236. doi:10.1093/pan/mpl013
- Stuart EA. Matching methods for causal inference: A review and a look forward. Stat Sci. 2010 Feb 1;25(1):1-21. doi: 10.1214/09-STS313. PMID: 20871802; PMCID: PMC2943670.
- Vable AM, Kiang MV, Glymour MM, Rigdon J, Drabo EF, Basu S. Performance of Matching Methods as Compared With Unmatched Ordinary Least Squares Regression Under Constant Effects. Am J Epidemiol. 2019 Jul 1;188(7):1345-1354. doi: 10.1093/aje/kwz093. PMID: 30995301; PMCID: PMC6601529.
- Iacus, S., King, G., & Porro, G. (2012). Causal Inference without Balance Checking: Coarsened Exact Matching. Political Analysis, 20(1), 1-24. doi:10.1093/pan/mpr013
Perhaps the biggest challenge we all face in epidemiologic research is recruitment of study participants. And recruiting a diverse population for our studies that allows for broad generalizability and transportability of effect estimates is something we haven’t done a good enough job of and as a consequence, our work has suffered. While we may think of this as not a methods issue, Dr. Jonathan Jackson helps us understand why representativeness affects or work and how we can do better.