SERious EPI

Sue Bevan - Society for Epidemiologic Research
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SERious EPI

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.

  1. NOV 15

    S4E4: Mediation with Kara Rudolph and Ivan Diaz

    In this episode, Matt and Hailey talk with Dr. Kara Rudolph and Dr. Ivan Diaz about mediation analysis. We talk through what it is, what it means and when we want to do it. We talk about mechanism of causation and how mediation can help. We cover things like natural direct and indirect effects and controlled direct effects (and why there isn’t a controlled indirect effect – a thing that stumped Matt for some time). And we discuss the different assumptions need to draw valid inferences in a mediation analysis, like all the many no confounding assumptions and the cross world assumption. And we talk about what Matt refers to as mediated moderation (interaction in the effect on the outcome between the exposure and mediator).

    59 min

  2. OCT 15

    S4E3: How do we define efficiency?

    In this episode, Hailey and Matt continue on their discussion on study efficiency and realize that we think about efficiency in very different ways. We talk about the difference between statistical efficiency and cost efficiency and we each make our case for one of them being the driving force in how we design and analyze studies. It may be the biggest disagreement we’ve had yet (though maybe that was interaction).We talk about matching and its impact of efficiency and also why we do matching. And we try to understand when matching is useful. Studies mentioned in the podcast: Rothman KJ, Poole C. A strengthening programme for weak associations. Int J Epidemiol. 1988 Dec;17(4):955-9. doi: 10.1093/ije/17.4.955. PMID: 3225112.

    53 min

  3. SEP 15

    S4E2: Study Efficiency with Robert Platt

    In this episode we are joined by Professor Robert Platt of McGill University to talk about study efficiency and the ways we can think about this in terms of study design. We talk about hierarchies of evidence and its relationship to things like target validity. We get into why we think case control studies are so often misunderstood, particularly with respect to missing that they should be nested within a cohort.  We talked about the varying definitions of efficient (variance, efficiency of confounding control, cost efficient, etc.) and how they relate to different study designs, and we disagreed about which definition is the most useful. And we talk about sampling and how it affects study efficiency and also what question we are asking. The paper that Rob reads over and over is: Kurth T, Walker AM, Glynn RJ, Chan KA, Gaziano JM, Berger K, Robins JM. Results of multivariable logistic regression, propensity matching, propensity adjustment, and propensity-based weighting under conditions of nonuniform effect. Am J Epidemiol. 2006;163:262-70. We also referenced: Westreich D, Edwards JK, Lesko CR, Cole SR, Stuart EA. Target Validity and the Hierarchy of Study Designs. Am J Epidemiol. 2019;188:438-443. Kramer MS, Guo T, Platt RW, Shapiro S, Collet JP, Chalmers B, Hodnett E, Sevkovskaya Z, Dzikovich I, Vanilovich I; PROBIT Study Group. Breastfeeding and infant growth: biology or bias? Pediatrics. 2002;110(2 Pt 1):343-7.

    52 min

  4. SEP 3

    S4E1: We’re Baaaaack… A Season 4 Preview

    We kick off season 4 by reminiscing about the origins of the podcast and preview what’s upcoming for season 4 where we will continue on our last season of reviewing Modern Epidemiology 4th edition. We touch on a few of the topics we are most excited about for the coming season and we preview some small formatting changes. But then we put each other through the fun questions that we ask our guests so you all can get to know us better (spoiler: Matt has no idea what the word non-fiction means). We are excited for our upcoming guests this season and the fun conversations we have in store.

    36 min

  5. JAN 30

    S3E12: Start with the questions that are easy to answer and then move on to the more challenging questions

    It’s hard to believe this is the final episode of season 3! In this season finale episode, we continue our discussion of topics related to Chapter 26 in Modern Epidemiology (4th Edition) with Dr. Eric Tchetgen Tchetgen. In this conversation we ask Dr. Tchetgen Tchetgen to help us better understand several issues related to interaction, including why it’s so important to study interaction.  He provides a helpful framework for thinking about interaction: start simple and then move on to more complex questions. As part of this framework, he emphasizes the distinction between total effects and main effects, how confounding plays into conversations about interaction, and the role of scale dependence when interpretating interaction.

    41 min

  6. JAN 15

    S3E11: You say tomato, I say tom-ah-to: a (somewhat) head-spinning discussion about interaction analyses

    Matt and Hailey take a deep dive into Chapter 26 in Modern Epidemiology, 4th Edition, Analysis of Interaction. This episode needs a content warning- it is among the most advanced and conceptually complex topics we have ever covered on SERious Epi. Interaction occurs when the effect of one exposure on outcome depends in some way on the presence or absence of another exposure. Seems like a simple enough concept, right? However, as you’ll see in this episode, there are many different layers of complexity to consider related to terminology, scale, and interpretation of interaction analyses.  A note from Matt and Hailey: since this material is very complex, we reached out to Dr. Jay Kaufman for his perspective on the episode before releasing it. He had some very helpful thoughts, and we would like to share them with you (paraphrasing with his permission):  Part of what is confusing about this topic is the terminology differences, with Hailey using terminology (“interaction”) that lines up with that used by VanderWeele, ME4, and the Hernán and Robins textbook chapter and Matt using terminology (“interdependence”) from other articles in the literature, such as Greenland and Poole (1988). When there are joint effects that are exactly multiplicative, or supermultiplicative, you know it’s a causal interaction (i.e., synergistic or biologic interaction) because multiplicativity is necessarily super-additive as long as both exposures meet consistency, exchangeability, and positivity assumptions. However, knowing that joint effects are submultiplicative  is not informative about additive interaction or synergism. It is also not possible to make a conclusion about additive interaction when a results section tells you only that in a logistic or Cox regression analysis there is “no significant interaction effect (p0.05)” as that just tells you an effect is not exactly multiplicative. Multiplicativity has some causal implications because it is super additive as long as the causal assumptions listed above are plausibly satisfied. There are several proposed causal mechanisms that would generate multiplicative joint effects especially from the cancer epidemiology literature (e.g., Koopman 1990). In general,  considering interaction on the additive scale is more useful for assessing public health relevance (e.g. Panagiotou and Wacholder 2014). Some of these concepts are difficult to convey in podcast format, so we’re including some helpful resources for anyone interested in learning more about this topic. Thanks again to Dr. Kaufman for helping us put this list together: Greenland S, Poole C. Invariants and noninvariants in the concept of interdependent effects. Scand J Work Environ Health. 1988 Apr;14(2):125-9. doi: 10.5271/sjweh.1945. PMID: 3387960. VanderWeele TJ. On the distinction between interaction and effect modification. Epidemiology. 2009 Nov;20(6):863-71. doi: 10.1097/EDE.0b013e3181ba333c. VanderWeele TJ. The Interaction Continuum. Epidemiology. 2019 Sep;30(5):648-658. doi: 10.1097/EDE.0000000000001054. PMID: 31205287; PMCID: PMC6677614. Greenland S, Poole C. Invariants and noninvariants in the concept of interdependent effects. Scand J Work Environ Health. 1988 Apr;14(2):125-9. doi: 10.5271/sjweh.1945. PMID: 3387960. Koopman JS, Weed DL. Epigenesis theory: a mathematical model relating causal concepts of pathogenesis in individuals to disease patterns in populations. Am J Epidemiol. 1990 Aug;132(2):366-90. doi: 10.1093/oxfordjournals.aje.a115666. PMID: 2372013. Panagiotou OA, Wacholder S. Invited commentary: How big is that interaction (in my community)--and in which direction? Am J Epidemiol. 2014 Dec 15;180(12):1150-8. PMID: 25395027.

    47 min

  7. JAN 9

    S3E10: Time-varying everything everywhere all at once

    In this episode, we are joined by Dr. Sonia Hernandez Diaz for a discussion on Chapter 25 in Modern Epidemiology, 4th edition. This chapter is focused on methods for causal inference in longitudinal settings, with a particular focus on time varying exposures. Dr. Hernandez-Diaz helps to explain some of the conceptual and methodological challenges related to time-varying exposures, including the advanced analytic strategies required and the careful conceptual considerations about defining the exposure of interest and causal questions. Papers referenced in this episode: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3731075/ https://academic.oup.com/aje/article/183/8/758/1739860

    48 min

  8. 10/31/2023

    S3E9: Feedback loops? Feedback spirals? Disentangling what we know about time-varying exposures.

    This episode is focused on Chapter 25 of Modern Epidemiology 4th edition, Causal Inference with Time Varying Exposures. In this episode, Matt and Hailey talk about how we should think about exposures that change over time. We discuss the concept of feedback loops- scenarios where the exposure affects outcome which affects a later time point of exposure and then that exposure affects a later outcome. We think about whether biologic (mechanistic) conceptualizations of feedback loop the same as the epidemiologic notion presented in the chapter. We then follow the chapter to continue our discussion about how time varying exposures change our frameworks for thinking about causal inference and analytic strategies (e.g., marginal structural models, g-formula, and structural mean models). A historical note about Andrew James Rhodes, whose picture is hanging up in the conference room that Hailey was recording from: https://discoverarchives.library.utoronto.ca/index.php/rhodes-andrew-james

    40 min
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About

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.

Information

  • Creator
    Sue Bevan - Society for Epidemiologic Research
  • Years Active
    2020 - 2024
  • Episodes
    53
  • Rating
    Clean
  • Copyright
    © 2020 Society for Epidemiologic Research - SER
  • Show Website
    SERious EPI

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