Show Notes
In this episode, we discuss Agent Based Models with Dr. Brandon Marshall of the Brown School of Public Health. We talk about what these models are and why they are so useful in epidemiology. We discuss the challenges with these models and how to improve them. We talk about microsimulations and their relationship to mathematical models like SIR models. We talk about how the fit into the world of predictive models but also how they relate to counterfactuals. We talk about how to account for bias in the inputs in these models and how they relate to DAGs and data generating mechanisms. We talk about all the skills needed to create ABMs (coders, modelers, epidemiologists, etc.) and the software used to create them as well as the challenges with and need for replication, calibration and validation. And we talk about how far outside of Matt and Hailey’s experience in epidemiology.
More from SERious EPI
In this season finale, Matt and Hailey reflect on what they learned, and are still wrestling with, after working through Part I of Causal Inference: What If. They discuss how revisiting familiar concepts can reveal new layers of nuance, why the current enthusiasm for target trial emulation shouldn’t be mistaken for a shortcut to causal inference, and the importance of recognizing when the available data or scientific question simply doesn’t support a target trial. The conversation turns to the growing role of AI in epidemiologic analysis and the risks of being able to generate sophisticated analyses without fully understanding what is happening behind the scenes. They also preview the discussion about advanced methods awaiting them in Season 6. And because no season finale would be complete without settling the truly important questions, they finish by deciding which epidemiologic biases best correspond to Friends characters, ice cream flavors, decades, Muppets, and statistical software.
In this episode of SERious Epidemiology, Hailey and Matt are joined by Lucy D’Agostino McGowan to discuss Chapter 10 of Causal Inference: What If? and the problem of random variability. This episode explores the distinction between random error and systematic bias, what confidence intervals and standard errors actually tell us, and why increasing sample size can improve precision without making a biased estimate any closer to the truth. Lucy offers a statistician’s perspective on why random error may actually be the easier problem, and why epidemiologists should be more concerned about where an estimate is centered than about squeezing out a little more efficiency. We also discuss topics like replication, matching versus weighting, baseline adjustment in randomized trials, p-values in Table 1, and the persistent temptation to overinterpret statistical significance.
In this episode of SERious Epidemiology, Hailey and Matt are joined by Dr. Louisa Smith to discuss Chapter 8 of Causal Inference: What If on selection bias. The conversation explores how selection bias can arise through conditioning on a collider, how it differs from confounding, and how loss to follow-up and censoring can introduce bias even in randomized trials. A major theme of this episode is the relationship between selection bias and confounding. We also discuss generalizability, target populations, competing events such as death. Finally, we talk about how challenging it is to address selection bias via analytic techniques including inverse probability weighting. This episode serves as a good reminder that when it comes to selection bias, an ounce of prevention is worth a pound of cure.
In this episode, we talk with Dr. Alexis Reeves about Chapter 9 of Causal Inference: What If, focusing on measurement bias (the bias formerly known as information bias). Measurement bias arises when exposures, outcomes, confounders, or colliders are measured incorrectly. We discuss different types of measurement bias: differential, nondifferential, dependent, and independent, and errors in measurement of continuous variables vs. categorical variables. We follow the structure of the chapter, next discussing DAGs and time-related issues in measurement of variables. We end the episode considering whether accurate measurement should be treated as its own causal identification assumption and by highlighting that mismeasured confounders deserve more attention because adjusting for them may leave residual bias or even worsen bias.
“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.