S3E8: 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:

S3E8: Maybe censoring is the least of your worries?

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.

S3E7: Are time to event analyses the Space Mountain of epidemiology?

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!

S3E6: Stratification with Rich MacLehose: Should you have Bert or Ernie pick you up from surgery?

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.

S3E5: Should I memorize the Mantel Haenszel formula?

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.

S3E4. Selecting people or selecting data: exploring different aspects of selection bias

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!


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.

S3E3. How do we deal with the people who never made it into our study?

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!

S3E2: Should we try to ensure misclassification is non-differential? Discussing measurement error with Dr. Patrick Bradshaw

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.

S3E1: Are we measuring what we think we’re measuring?

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.

S2E16: There’s a 95% probability you’ll enjoy learning about sample size and precision with Dr. Jon Huang

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.