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!
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.