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.

In this episode we follow up on our conversation with Tim Lash on Quantitative Bias Analysis (QBA), something both Hailey and I have experience with. We talk about what QBA is, why you would want to use it and for what sources of bias it is most applicable. We talk about our own experience with QBA and when we find it most useful. We talk about cases where lots of measurement error leads to little bias and cases where small amounts of measurement error leads to lots of bias. We talk about the overused phrase “non-differential bias towards the null” and why we both hate it. We discuss the impact of bias in terms of direction, magnitude and uncertainty in study results. We talk about the critiques of the methods and when QBA should be done. And we discuss what the role of peer review is (and if it should include QBA). And we discuss Matt’s whether our small talk is useful, our ability to time travel and whether naps are good or bad and if podcasts can nap.

In this episode we talk to Dr. Timothy Lash of Emory University about Quantitative Bias Analysis (QBA). We talk about how QBA is any method that quantifies the impact of non-random error. We talk about direction magnitude and uncertainty. We differentiate from sensitivity analysis, and we talk about how to identify key sources of bias. We talk about bias models and bias parameters and how we draw inferences from bias analyses. We talk about validation data and where you can get it. We talk about why predictive values often aren’t as useful as classification values for bias analysis. We talk about how bias analysis can strengthen your results and that our intuition about the impact of biases is t always great. And we talk about how bias analysis can guide your future research. We differentiate between simple and probabilistic bias analysis. And we end with some examples of cases where bias analysis is really helpful.

In this episode Hailey and Matt talk about Matt’s technology troubles (including having his computer just decide not to let him log on) before we discuss regression discontinuity and difference in difference approaches as part of quasi experimental methods. We focus on what quasi experimental means and encompasses and its relation to natural experiments. We talk about who owns interrupted time series (epidemiologists, economists, other social scientists?). Matt again admits he can’t define exogeneity. We talk about how both designs exploit a threshold when there is a rapid change in the probability of being exposed and we think of those on either side of the discontinuity close to the threshold are exchangeable and we can estimate effects in that population under a set of assumptions. And we talk about how difference in difference takes this same approach but adds a control group. And we debate whether the last difference is singular or plural.

In this episode we talk to Dr. Usama Bilal of Drexel University about Regression Discontinuity Design (RDD) and Difference-in-Differences (DiD), two quasi experimental methods that fall under the instrumental variables framework which we discussed in previous episodes. We talk about what RDD is, the different types (fuzzy vs sharp) and what we are actually estimating (LATE vs CACE). We talk about the bias vs variance tradeoff in how far from the threshold we choose to draw inferences. We talk about the assumptions that are needed for these methods to give valid estimate of effects. Then we talk about DiD and how this is a form of RDD with a second group that does not experience the discontinuity as a control. And we talk about the additional assumptions needed for this approach (e.g. parallel trends).

In this episode, Hailey and Matt discuss whether IVs are rebellious or magical or the midlife crisis of methods. We talk about how they deal with confounding problems. We talk about how they are used to attempt to mimic randomization and the assumptions for IVs. We talk about why it’s so helpful to think about who gets the exposure and why for causal inference. We talk about how IVs fit in with the target trial framework and wham it might tell us about how to teach intro epi. We talk about what estimand IVs estimate. And we relitigate the soda vs pop discussion.

In this episode, we discuss instrumental variables with Dr. Rita Hamad of Harvard’s TH Chan School of Public Health. This episode is focused on the first part of Chapter 28 of Modern Epidemiology 4th edition on quasi experimental methods. We start with what quasi experimental designs are and why we would want to use them (and whether more epidemiologists are being exposed to them). We also talk about why these methods are more common in economics than in epi. We talk about how these methods try to take advantage of something that approximates randomization to estimate causal effects. We talk about what instrumental variables are and the conditions required to be met for a variable to be an instrument. We focus on the strengths and limitations of the methods and when they make the most sense to use them. We talk about what happens when you violate the assumptions of IV. We talk about weak and strong IVs and we talk about Mendelian randomization and its role in epi. And we ask the age-old question, how do you find the elusive instrumental variable?

In this episode we follow up on our conversation about mediation. We talk about what mediation is and when it is useful. We talk about the history of these methods. We debate what direct and indirect effects are. We describe natural and controlled effects. We discuss the importance of the number 666 in Matt’s life. We talk about exposure mediator interaction.  Matt learns what kinesiology is. We discuss proportion mediated and proportion eliminated. And we talk about the confounding assumptions needed for mediation analysis.

In this episode, Matt and Hailey talk with Dr. Kara Rudolph and Dr. Ivan Diaz about mediation analysis. We talk through what it is, what it means and when we want to do it. We talk about mechanism of causation and how mediation can help. We cover things like natural direct and indirect effects and controlled direct effects (and why there isn’t a controlled indirect effect – a thing that stumped Matt for some time). And we discuss the different assumptions need to draw valid inferences in a mediation analysis, like all the many no confounding assumptions and the cross world assumption. And we talk about what Matt refers to as mediated moderation (interaction in the effect on the outcome between the exposure and mediator).

In this episode, Hailey and Matt continue on their discussion on study efficiency and realize that we think about efficiency in very different ways. We talk about the difference between statistical efficiency and cost efficiency and we each make our case for one of them being the driving force in how we design and analyze studies. It may be the biggest disagreement we’ve had yet (though maybe that was interaction).We talk about matching and its impact of efficiency and also why we do matching. And we try to understand when matching is useful.

Studies mentioned in the podcast: