SERious EPI Sue Bevan - Society for Epidemiologic Research

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.

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.

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

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

Recording from across the globe, in Melbourne, Australia, Dr. Margarita Moreno-Betancur joins us for an episode on Chapter 22 in Modern Epidemiology (4th edition) on Time-to-Event Analyses. This is a chapter focused on the methods we use when the timing of the occurrence of the event is of central importance. Dr. Moreno-Betancur answers all our questions about these types of analyses, including: the importance of the time scale, defining the origin (time zero), censoring vs. truncation. We also ask Dr. Moreno-Betancur to weigh-in on a hot take about whether the Cox Proportional Hazard model is overused in the health sciences literature.

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!