SERious EPI

“Confounding, Confounding, Confounding” is like the epidemiologist’s version of “Marcia, Marcia, Marcia” from the Brady Bunch. To discuss Chapter 7 of Causal Inference: What If, we welcome Dr. Ashley Naimi. In this chapter, we discuss confounding as a central problem when estimating causal effects from observational data. The chapter emphasizes that confounding is not just an imbalance in covariates across exposure groups, but a causal problem that depends on the underlying structure of how treatment, outcome, and other variables are related. In this episode, Dr. Naimi helps explain concepts related to confounding, exchangeability, and faithfulness. We (try to) talk through confounding-related DAGs and how they are a useful tool to understand confounding bias. This episode shows why confounding gets so much attention in epidemiology: it is everywhere, often misunderstood, and, like Marcia Brady, it has a way of stealing the spotlight.

In this episode, Hailey and Matt discuss whether IVs are rebellious or magical or the midlife crisis of methods. We talk about how they deal with confounding problems. We talk about how they are used to attempt to mimic randomization and the assumptions for IVs. We talk about why it’s so helpful to think about who gets the exposure and why for causal inference. We talk about how IVs fit in with the target trial framework and wham it might tell us about how to teach intro epi. We talk about what estimand IVs estimate. And we relitigate the soda vs pop discussion.

In this episode we follow up on our conversation about mediation. We talk about what mediation is and when it is useful. We talk about the history of these methods. We debate what direct and indirect effects are. We describe natural and controlled effects. We discuss the importance of the number 666 in Matt’s life. We talk about exposure mediator interaction. Matt learns what kinesiology is. We discuss proportion mediated and proportion eliminated. And we talk about the confounding assumptions needed for mediation analysis.

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.

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.

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 (p<0.05)” 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.

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

In this episode Matt and Hailey discuss Chapter 22 of the 4th edition of Modern Epidemiology. This is a chapter focused on time to event analyses including core concepts related to time scales, censoring, and understanding rates. We discuss the issues and challenges related to time to event analyses and analytic approaches in this setting including Kaplan Meier, Cox Proportional Hazards, and other types of fancy models that are frequently taught in advanced epi courses (e.g., Weibull, Accelerated Failure Time) but infrequently used in the real-world. The chapter ends with a brief discussion of competing risks. It’s clear that Matt and Hailey need to brush up on concepts related to competing risks and semi-competing risks, and fortunately next month we’ll have an expert join us to answer all of our questions!