EpiMonitor Summer Book Round-Up

Author: Madeline Roberts, PhD, MPH, and Katelyn Jetelina, PhD, MPH

Summer is in full swing, and whether you find yourself on a plane, at the beach, poolside, or otherwise, you may want an excellent read to take along on whatever adventures and endeavors you have in store. We’ve rounded up a few old and new books that we’ve been thinking about, and we hope they spark your interest and thinking!

Thinking, Fast and Slow

by Daniel Kahneman

https://bit.ly/3DpPmvq

Available in Kindle, Audio, Paperback & Hardcover formats

This book, written by Nobel Prize-winning economist Daniel Kahneman, is not about epidemiology per se but deals extensively with bias, heuristics, and systematic error in cognition. Startlingly insightful, Kahneman’s work on how we tend to make decisions and how to make better ones is a must-read. This book was required reading for a class during my (Madeline) MPH, and one that I have returned to on several occasions in the time since to reflect on my decision-making processes.

Mark White

https://amzn.to/43zmeNd

Available in Kindle & Paperback formats

Written by epidemiologist and former CDC Director of International Health, Dr. Mark White, this book follows the harrowing, real-life experiences that comprised Dr. White’s career as an infectious disease specialist. The reflections on pandemics and life experiences from a true expert in disease investigation include his Ebola and HIV surveillance work in the Philippines and Uganda, where he encountered numerous natural disasters and political instability.

- Books cont'd on page 3

Lessons from the Covid War: An

Investigative Report

by The Covid Crisis Group

https://amzn.to/44S5eCY

Available in Kindle, Audio & Paperback formats

Now that the COVID-19 emergency is over, we need a national commission to review what the United States got right, what we got wrong, and how we can prepare for the future, just like the 9/11 Commission. Unfortunately, it doesn’t look like this will happen due to the politicized landscape. This book is the next best thing. The commissioner for 9/11 conducted hundreds of interviews to understand lessons learned from experts around the country. Each chapter dives deep and accurately depicts the downfalls and successes. A fantastic read that every American should read, particularly those of us in public health.

Pathogenesis: A History of the World in Eight Plagues

by Jonathan Kennedy

https://amzn.to/3K4Bw5G

Available in Kindle, Audio & Hardcover formats

A thorough delve into history and evolution through the lens of disease, Pathogenesis (written by a sociologist in 2023) is an engaging read on how outbreaks have shaped human events. From military conquests to labor practices, Kennedy makes a case for how infectious disease has often functioned as a “devastating weapon of mass destruction,” which has substantially contributed to and interfered in human history. After reading through thousands of years of the ravages of infectious disease, Kennedy’s call for collective political will to organize against the next pandemic should certainly register with readers.

Plagues and Peoples

by William H. McNeill

https://amzn.to/3Q6UCf9

Available in Kindle, Paperback & Hardcover formats

For a several-decades-earlier historical take on world history in disease, Plagues and Peoples (written by a historian in 1976) offers a comprehensive look at the implications of disease throughout human history and covers everything from ecology disruption and politics to migration and societal development. Science has advanced since 1976. However, this book came to mind not only because of the recent release of Pathogenesis but also because of the underlying premise: “…in any effort to understand what lies ahead, the role of infectious disease cannot properly be left out of consideration."

Creating Great Choices: A Leader's Guide to Integrative Thinking

by Jennifer Riel, Roger L. Martin

https://amzn.to/43znM9Z

Available in Kindle, Audio & Hardcover formats

Given the substantial upheaval over the past few years in virtually every sector, particularly the current state of public health, this book came to mind for rebuilding and creating a path forward. This book offers a framework for choices with several practical exercises, not just for leaders in the title but for anyone thinking critically about their organization or career. Perhaps the most valuable element of this book is that it presents an alternative to all-ornothing thinking, namely, taking the best aspects from opposing options and creating a new choice—and that this is a skill you can practice and cultivate. We can’t think of a more needed skill in the current age.

Author: Madeline Roberts, PhD, MPH

This month we were delighted to catch up with Dr. Maya Mathur who gave several fantastic presentations covering causal inference and Evalues at the recent SER conference. Dr. Mathur is an Assistant Professor in Stanford University’s Quantitative Sciences Unit and the Department of Pediatrics. She also serves as the Associate Director of Stanford Data Science’s Center for Open and Reproducible Science (CORES). We enjoyed following up with Dr. Mathur about everything from open science and p-hacking to advice for early career epidemiologists and data scientists.

EpiMonitor: Could you share a bit about your current research interests? Are there any projects about which you are particularly excited?

MM: For better or worse, I'm a greedy algorithm: I tend to jump into whatever research topic happens to most excite me at any given moment, so I do tend to jump around. On the epi side, I've been very interested recently in selection bias and missing data, and how graphical models can help straighten out these counterintuitive issues. I'm enjoying delving into some new-to-me theory and proof techniques based on graphical models; it's all very confusing and interesting, an addictive combination. I also have a longerstanding interest in evidence synthesis and meta-analysis. Recently I've been very interested in meta-analytic methods to address p-hacking, which, it turns out, is a lot more pernicious than what we usually conceptualize as publication bias.

EpiMonitor: A PubMed search shows a substantial increase in the number of publications mentioning

E-values over the past ten years. Can you talk about the utility of E-values in causal inference and why you think there may have been such a rise in their use?

[Editor’s note: an E-value can be defined as “the minimum strength of association on the risk ratio scale that an unmeasured confounder would need to have with both the exposure and the outcome, conditional on the measured covariates, to fully explain away a specific exposure-outcome association.” (https://www.evalue-calculator.com/)

MM: I think their utility is as an easy-to-apply sensitivity analysis that is conservative in the sense that it allows for worst-case confounding. E-values certainly have their limitations as well, particularly in relation to their conservatism, though I think Peng Ding

- Mathur cont'd on page 6 and Tyler [VanderWeele, both co-authors on the Website and R package for computing E-values] have been thoughtful about trying to convey this in their various papers. In terms of uptake, I think it helps that some top journals are encouraging or requiring their use, and there is software to easily calculate E-values, so it is not a big lift. It's also pretty interesting that approaches very similar to the E-value can be used for many other forms of bias that manifest as some kind of backdoor path in a graph; the theory Peng developed for the E-value is general and flexible in this way.

EpiMonitor: Some of your work focuses on metaanalyses, which are useful in identifying patterns across the literature, but their utility can be restricted by publication bias. Can you talk about your work on P-hacking and the corresponding R package phacking, and how it addresses some of the issues arising from publication bias?

MM: What's fascinating about p-hacking is that we often talk about it alongside publication bias as if they're interchangeable, but they really are not. As traditionally conceived, publication bias is when there is a filter on which studies are published and available for inclusion in metaanalysis. But p-hacking is when investigators actually manipulate results within their studies, for example to obtain a significant result. The bias that arises from p-hacking is, it turns out, much harder to deal with statistically than the bias that arises from pure publication bias. The paper you're referring to provides some new meta-analytic methods that will be unbiased under many but not all forms of p-hacking and publication bias, unlike methods that only address publication bias. The R package and accompanying website (metabias.io) are largely thanks to my postdoc Mika Braginsky and I think goes a long way toward making the methods accessible.

EpiMonitor: Your personal website is a wealth of information with links to datasets, code, and lecture slide decks. You are clearly a proponent of open science! Can you talk a bit about what open science means to you and why it's important? Have you encountered any drawbacks?

MM: Yes, I believe that in general open science can lead to a much more reliable, efficient, and accurate scientific ecosystem. Some of the key tenets that I most strive for in my own work are making analysis code publicly available, making de-identified data available when permissible, and preregistering study hypotheses and protocols. There is increasingly compelling empirical evidence from other disciplines, especially the social sciences, that these kinds of practices can reduce errors and publication bias in the literature, and I hope to see our field update its norms and incentives accordingly. There can definitely be drawbacks to adopting open-science practices: for example, it's harder to p-hack and get attractive results to publish! On the other hand, I do find that pre-specifying analyses sharpens my scientific reasoning and that organizing data and code for public release encourages better, more reuseable workflows. Sometimes there are surprises as well. In a paper Matt Fox and I wrote, we recount a story where the public repo containing study materials for one of my papers became to my great surprise pretty popular for re-use in other papers. This not only meant people were citing the original paper a lot more than I'd expected, but also it was very fun to see the creative new ideas and results that others generated from the study materials.

EpiMonitor: You completed your PhD in 2018, do you have any advice for early career epidemiologists and data scientists?

- Mathur cont'd on page 7

-Harris County cont'd from page 6

MM: Yes, that's right, so probably my advice should be treated as untested and unreliable until further study! More seriously, for me an important guiding principle has been to keep at the forefront why I love being in academia in the first place, and relatedly, what I need in order to do work that contributes usefully to our canon of knowledge. Then I try to prioritize the most mission-critical things. I love going off into a hermit cave and struggling with a difficult concept or proof. This is the main reason I am in academia. It would be easy to let meetings and email constantly interrupt this kind of deep work time, but I try hard to prevent this from happening too often. Similarly, curiosity is vital to life in academia but can easily get pushed aside if we focus just on getting papers out and checking off tenure criteria. If I get interested in something, I try to just roll with it even if it means writing a paper, or recently founding a lab, on a topic that is pretty out of the box. Last, of course, it's also helpful to try to have a life outside of work. I work strange hours, but do preserve a significant amount of time to enjoy friendships and several hobbies, and I think this makes the rigors of academia sustainable in the long run.

Dr. Mathur’s work, including links to publications, data repositories, and slide decks, can be found on her personal website: https://www.mayamathur.com/. ■