SERious EPI

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 (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.

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!

In this episode we discuss Chapter 18 in the Modern Epidemiology (4th Ed) textbook focused on stratification and standardization with Dr. Rich MacLehose. We invited the illustrious Dr. MacLehose to be the guest for this chapter because it is one of the most important in the book, linking the theoretical concepts discussed in the early chapters with the advanced analytic techniques discussed in subsequent chapters. In this episode we cover topics such as standardization, stratification, pooling, the use and interpretation of relative and absolute effect estimates, and p-values to evaluate effect heterogeneity.

This is an episode focused on ME4 Chapter 18 (Stratification and Standardization). This is a pretty formula-heavy chapter and I’m sure all of our listeners are tuning in to hear Matt’s voice read them to you: “The sum of M1i times T0i….”. So sorry to disappoint, but instead, we focused this issue on big picture conceptual issues discussed in the chapter. Matt and Hailey talk about the importance of stratification, compare pooling and standardization, discuss Mantel Haenszel and maximum likelihood estimation, and then finish the episode talking about homogeneity and heterogeneity.

In this episode we feature a super expert on all things related to selection bias, Dr. Chanelle Howe. There are a lot of confusing issues related to selection bias: how it’s defined, how it relates to collider stratification bias, whether it’s a threat to internal or external validity (or both!). Chanelle helps us understand many of the nuances related to selection bias and provides helpful resources for readers interested in learning more about the topic. Is a lack of exchangeability related to confounding bias or selection? How can DAGs help us decipher the difference between confounding bias and selection? Can you have selection bias in a prospective cohort study? Join us to find out the answers to all of these questions and much more! Resources: Hernán MA. Invited Commentary: Selection Bias Without Colliders. Am J Epidemiol. 2017 Jun 1;185(11):1048-1050. doi: 10.1093/aje/kwx077. PMID: 28535177; PMCID: PMC6664806. Lu H, Cole SR, Howe CJ, Westreich D. Toward a Clearer Definition of Selection Bias When Estimating Causal Effects. Epidemiology. 2022 Sep 1;33(5):699-706. doi: 10.1097/EDE.0000000000001516. Epub 2022 Jun 6. PMID: 35700187; PMCID: PMC9378569. Howe CJ, Cole SR, Chmiel JS, Muñoz A. Limitation of inverse probability-of-censoring weights in estimating survival in the presence of strong selection bias. Am J Epidemiol. 2011 Mar 1;173(5):569-77. doi: 10.1093/aje/kwq385. Epub 2011 Feb 2. PMID: 21289029; PMCID: PMC3105434.

In this episode, Matt and Hailey discuss all things selection bias. This chapter on selection bias and generalizability is the shortest of the bias chapters in the Modern Epidemiology textbook. Does that mean it’s the simplest? Listen to this episode and decide for yourself!

In this episode we have a conversation with Patrick Bradshaw about issues related to measurement error, misclassification, and information bias. We ask him to help define and clarify the differences between these concepts. We chat about dependent and differential forms of misclassification and how helpful DAGs can be for identifying these sources of bias. Patrick helps to explain the problem with the over-reliance on non-differential bias producing a bias toward the null and concerns about being “anchored to the null” in epidemiologic analyses. This episode will also serve to provide you with the most up-to-date information from Patrick on his recommendations about excellent new TV shows to stream (Wednesday on Netflix; Wandavision on Disney+). Two thumbs up.

In the season three premiere Matt and Hailey discuss Chapter 13 in Modern Epidemiology, 4th edition. For the third season of the SERious Epi podcast, we are going to continue our close-reading of the newest version of the Modern Epi textbook. This chapter is focused on measurement error and misclassification. In this episode we discuss issues related to the mis-measurement of exposure, outcome, and covariates. We also debate whether misclassification is just an analytic issue (i.e., putting people into the wrong categories) or an analytic + conceptual issue (i.e., putting people into the wrong categories and having an incorrect definition for those categories). We also talk about measurement error DAGs, why we wish more people use analytic approaches to correct for measurement error, and Matt explains the concept of email bankruptcy.

In this episode of Season 2 of SERious Epidemiology, Hailey and Matt connect with Dr. Jon Huang for a discussion on precision and study size. We wade into whether or not we should use p-values. We discuss whether the debates on p-values are real or just on Twitter and whether they should be used in observational epi or just in trials. We ask whether p-values do more harm than good in observational studies or whether the harm is really around null hypothesis significance testing. We talk about misconceptions about p-values. And Jon tells us how he’s going to win a gold medal in the Winter Olympics, despite living in a tropical climate.

In this episode of Season 2 of SERious Epidemiology, Hailey and Matt finally start talking about random error. We explore the deep philosophical (as deep as we are capable of) meaning behind randomness and whether the universe is a random (and hey, while we are at it, is there even free will) and how we think about random error. We talk about p-hacking and p-curves and anything p really. And we talk about precision and accuracy in epidemiologic research. And Hailey aces Matt’s quiz.

In this episode of Season 2 of SERious Epidemiology, Hailey and Matt connect with Dr. Maya Mathur for a discussion on confounding. We talk about different ways of thinking about confounding and we discuss how different sources of bias can come together. We talk about overadjustment bias, a topic we all feel needs more attention. We discuss e-values, and have Dr. Mathur explain their practical utility and also how complicated they are to interpret. And we discuss bias analysis for meta-analyses. Article mentioned in this episode: Schisterman EF, Cole SR, Platt RW. Overadjustment bias and unnecessary adjustment in epidemiologic studies. Epidemiology. 2009 Jul;20(4):488-95. doi: 10.1097/EDE.0b013e3181a819a1. PMID: 19525685; PMCID: PMC2744485.

In this episode of Season 2 of SERious Epidemiology, Hailey and Matt discuss confounding and whether confounding is hogging the spotlight in epi methods and epi teaching. We debate the value of all the different terms for confounding in the world of epi and beyond and struggle to define them all. We talk about different definitions for confounding and we differentiate between confounders and confounding. We talk about the 10% change in estimate of effect approach and its limitations and we talk about different strategies for confounder control. And Hailey coins the term “DAGmatist”. We reference the paper below: VanderWeele, T.J. and Shpitser, I. (2011). A new criterion for confounder selection. Biometrics, 67:1406-1413.

In this episode of Season 2 of SERious Epidemiology, (recorded back when we were getting COVID booster shots) Hailey and Matt connect with Dr. Ellie Matthay for a discussion on Chapter 8 on case-control studies. We finally answer whether it is spelled with a – or not (and Hailey and Ellie disagree with Matt about semicolons). We discuss how cohort studies and case control studies differ and overlap. We talk about whether case control studies are more biased than cohort studies. And Hailey reveals her dreams for releasing Modern Epidemiology: the Audiobook (with possible singing).

In this episode of Season 2 of SERious Epidemiology, Hailey and Matt get into the humble case control study. We discuss the ins and outs of this much maligned study design that has so flummoxed so many in epidemiology. We ask the hard questions about the best way sample in a case control study, whether we spend too much or not enough time on it in our teaching, whether a case control study always has to be nested within some hypothetical cohort, whether the design is inherently more biased than cohort studies (spoiler: no, but…), why some people refer to cases and controls when they are not referring to a case control study, and, if it were on a famous TV show, which character the case control study would be (and more importantly, why Hailey has never seen said TV show). Papers referenced in this episode: Selection of Controls in Case-Control Studies: I. Principles Sholom Wacholder, Joseph K. McLaughlin, Debra T. Silverman, Jack S. Mandel American Journal of Epidemiology, Volume 135, Issue 9, 1 May 1992, Pages 1019–1028, https://doi.org/10.1093/oxfordjournals.aje.a116396 Selection of Controls in Case-Control Studies: II. Types of Controls Sholom Wacholder, Debra T. Silverman, Joseph K. McLaughlin, Jack S. Mandel American Journal of Epidemiology, Volume 135, Issue 9, 1 May 1992, Pages 1029–1041, https://doi.org/10.1093/oxfordjournals.aje.a116397 Selection of controls in case-control studies. III. Design options S Wacholder 1, D T Silverman, J K McLaughlin, J S Mandel Wacholder S, Silverman DT, McLaughlin JK, Mandel JS. Selection of controls in case-control studies. III. Design options. Am J Epidemiol. 1992 May 1;135(9):1042-50. doi: 10.1093/oxfordjournals.aje.a116398

In this episode of Season 2 of SERious Epidemiology, Hailey and Matt get some real world experience with cohort studies in a conversation with Dr. Vasan Ramachandran, PI of the Framingham Heart Study (FHS). FHS is a very well-known cohort study and the model that many of us have in mind when we think of cohort studies. We get a bit of history on FHS and Hailey and I have a chance to ask the questions we have struggled with around cohort studies including the role of representativeness. And, spoiler alert, we learn that FHS did not invent the term “risk factor” as Matt has been telling his students for years.

In this episode of Season 2 of SERious Epidemiology, Hailey and Matt get into cohort studies. We spend a lot of time confessing our limitations, both personally, and as a field, in assigning person time. We talk about the end of the large cohort study and the challenges in determining when to consider a person as exposed. We talk about issues of immortal person time and whether it is technically acceptable to include those who already have the outcome in a cohort study.