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.
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47:42
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47:42
S4E10: Quantitative Bias Analysis with Dr. Tim Lash
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.
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58:55
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58:55
S4E9: Regression Discontinuity and Difference in Difference(s?)
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.
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52:16
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52:16
S4E8: Regression Discontinuity and Difference-in-Differences with Dr. Usama Bilal
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).
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55:38
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55:38
S4E7: Instrumental Variables
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.
SERious EPI is a podcast hosted by Hailey Banack and Matt Fox where leading epidemiology researchers are interviewed on cutting edge and novel methods. Interviews focus on why these methods are so important, what problems they solve, and how they are currently being used.
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