Doctoral Seminar (EPIB 706, Fall 2020)

Course Description

EPIB 706 is a PhD-level seminar aimed at providing space for students to engage with overarching concepts critical to the theory and practice of epidemiology, as well to explore recent controversies and debates in the field. The purpose of this course is not to equip you with any marketable skill; rather, it is to reinforce your formal coursework by making space to develop and sharpen your critical thinking skills. We will review a selection of papers that range across methods, principles, arguments, and debates in epidemiology and the wider scientific community.

Eligibility

Registration in the PhD program in Epidemiology and successful completion of the course sequence in epidemiologic methods (EPIB 704 and EPIB 705) is required. Students who have not completed EPIB 704 and EPIB 705 must obtain the instructor’s permission to take the course.

Course Format

This is a discussion-based course and this year, due to the ongoing coronavirus pandemic, it will be held remotely until further notice. We are all here to learn from one another, and this course simply won’t work without engagement and participation from all of us (including me). Of course, everyone has their own level of comfort speaking up, as well as varying levels of interest in some of these topics, so I have no expectation that everyone participates equally. What I do ask is that you make a sincere effort to engage with the material, both in terms of the reading and in the discussion forum. Learning how to respectfully express your opinion about conceptual and methodological issues is a core part of being a scientist.

Evaluation

Written Assignments

The discussions in the course are meant to activate your critical thinking skills, and to encourage you to synthesize your own thoughts on the material, particularly as it may relate to your area of research interest. Toward that end, over the course of the semester you will be asked to submit two critical essays that explore a topic of relevance to epidemiologic science.These may be direct responses to material that we read or discuss in class, or they may be essays exploring other topics relevant to your work that demonstrate good-faith efforts to engage with the class material. These should take the form of a commentary similar (in spirit) to those we have read during the semester, and should be no longer than 1500 words. The first one is due on October 22, 2020 and the second one is due on December 7, 2020. I will provide examples of what I think are good pieces of writing to aspire to.

Class Participation

In addition to the writing assignment, each student will be asked to lead one day of discussion among the topics that we will cover. For that session, you will come prepared to briefly summarize the material we have read, and to prepare some discussion points to help keep the conversation moving. I have created a Google spreadsheet with the current days for each topic here. Please sign-up for a session and we can have a discussion about the readings and where to draw on other resources for the topic.

Grading

The course is pass-fail.

Reading

The assigned readings are the core of the course material, and students are expected to carefully and critically read each assignment before class. To facilitate student engagement with the reading we will use the online tool Perusall for all required readings. Perusall is a reading platform in which students annotate texts collaboratively alongside one another. More information on how Perusall works and how it is integrated into the course is available here (thank you Arizona State!). To access Perusall through MyCourses, navigate to Content > Perusall (readings) > Perusall, and then click the “Open Link” button. This will take you to the Perusall site and automatically register you as a member of the course. If you are having any trouble accessing the readings through Perusall contact me right away. I will not be using Perusall’s grading features, but I expect you to read, post questions, respond to other students questions and answers, and to take an active role in generating productive discussion.

A Note About Class Participation

Participation means showing up for each class having read and engaged with the material assigned. It will help facilitate discussion if you could aim to contribute at least 2-3 points for discussion in Perusall, and bring those to class. During the discussion period in class participation means asking questions about anything in the readings that seems unclear or objectionable, offering respectful arguments and responses, and respectfully listening to the arguments and responses of others. Contributions should be relevant and helpful and demonstrate that you are engaging with the material being discussed at the time, and that you are well-prepared for class.

Course Outline (12 “questions” to consider)

A note about the outline. In an effort to make this course as dynamic and helpful to students as possible, the list of topics and reading below is subject to change. Enthusiasm (or lack thereof) for certain topics may lead us to revise, drop, add, or replace some readings or entire topics as we go. I promise to entertain any changes or suggestions, but may also disagree if I feel certain topics or readings are too important to replace.

Week 1: Course introduction

• Administrative aspects of the course.
• Round table – introductions.
• Discussion of objectives and competencies.

Week 2: What is the present and future of epidemiology?

Tuesday: Where does epidemiology stand today?

• Lau B, Duggal P, Ehrhardt S. Epidemiology at a time for unity. Int J Epidemiol 2018;47(5):1366–1371. (https://doi.org/10.1093/ije/dyy179)

• Lesko CR, Keil AP, Edwards JK. The Epidemiologic Toolbox: Identifying, Honing, and Using the Right Tools for the Job. Am J Epidemiol 2020;189(6):511–517. (https://doi.org/10.1093/aje/kwaa030)

Thursday: Where should epidemiology be going?

• Glymour MM, Bibbins-Domingo K. The Future of Observational Epidemiology: Improving Data and Design to Align With Population Health. Am J Epidemiol 2019;188(5):836–839. (https://doi.org/10.1093/aje/kwz030)

• Diez Roux AV. The Unique Space of Epidemiology: Drawing on the Past to Project Into the Future. Am J Epidemiol 2019;188(5):886–889. (https://doi.org/10.1093/aje/kwz001)

• Kuller LH. Epidemiologists of the Future: Data Collectors or Scientists? Am J Epidemiol 2019;188(5):890–895. (https://doi.org/10.1093/aje/kwy221)

• Davey Smith G. Post-Modern Epidemiology: When Methods Meet Matter. Am J Epidemiol 2019;188(5):1410–1419. (https://doi.org/10.1093/aje/kwz064)

Week 3: How should we be asking questions?

Tuesday: Where do epidemiologic questions come from?

• Fox MP, Edwards JK, Platt R, Balzer LB. The Critical Importance of Asking Good Questions: The Role of Epidemiology Doctoral Training Programs. Am J Epidemiol 2020;189(4):261–264. (https://doi.org/10.1093/aje/kwz233)

• Vandenbroucke JP, Pearce N. From ideas to studies: how to get ideas and sharpen them into research questions. Clin Epidemiol 2018;10:253–264. (https://doi.org/10.2147/CLEP.S142940)

• Edwards JK, Lessler J. What Now? Epidemiology in the Wake of a Pandemic. Am J Epidemiol 2020 Jul. (https://doi.org/10.1093/aje/kwaa159)

Thursday: Are well-defined interventions necessary?

• Schwartz S, Gatto NM, Campbell UB. Causal identification: a charge of epidemiology in danger of marginalization. Ann Epidemiol 2016;26(10):669-673. (https://doi.org/10.1016/j.annepidem.2016.03.013).

• Hernan MA. Does water kill? A call for less casual causal inferences. Ann Epidemiol 2016;26(10):674-680. (https://doi.org/10.1016/j.annepidem.2016.08.016).

• Maldonado G. The role of counterfactual theory in causal reasoning. Ann Epidemiol 2016;26(10):681-682. (https://doi.org/10.1016/j.annepidem.2016.08.017).

• Kaufman JS. There is no virtue in vagueness: Comment on: Causal Identification: A Charge of Epidemiology in Danger of Marginalization by Sharon Schwartz, Nicolle M. Gatto, and Ulka B. Campbell. Ann Epidemiol 2016;26(10):683-684. (https://doi.org/10.1016/j.annepidem.2016.08.018).

• Schwartz S, Gatto NM, Campbell UB. Heeding the call for less casual causal inferences: the utility of realized (quantitative) causal effects. Ann Epidemiol 2017;27(6):402-405. (https://doi.org/10.1016/j.annepidem.2017.05.012).

Week 4: What do we mean by causal inference?

Tuesday: Are potential outcomes and DAGs necessary?

• Vandenbroucke JP, Broadbent A, Pearce N. Causality and causal inference in epidemiology: the need for a pluralistic approach. Int J Epidemiol. 2016 12;45(6):1776–1786. (https://doi.org/10.1093/ije/dyv341)

• Krieger N, Davey Smith G. The tale wagged by the DAG: broadening the scope of causal inference and explanation for epidemiology. Int J Epidemiol. 2016 12;45(6):1787–1808. (https://doi.org/10.1093/ije/dyw231)

• VanderWeele TJ. Commentary: On Causes, Causal Inference, and Potential Outcomes. Int J Epidemiol. 2016 12;45(6):1809–1816. (https://doi.org/10.1093/ije/dyw230)

• Daniel RM, De Stavola BL, Vansteelandt S. Commentary: The formal approach to quantitative causal inference in epidemiology: misguided or misrepresented? Int J Epidemiol. 2016 12;45(6):1817–1829. (https://doi.org/10.1093/ije/dyw227)

• Robins JM, Weissman MB. Commentary: Counterfactual causation and streetlamps: what is to be done? Int J Epidemiol. 2016 12;45(6):1830–1835. (https://doi.org/10.1093/ije/dyw231)

• Blakely T, Lynch J, Bentley R. Commentary: DAGs and the restricted potential outcomes approach are tools, not theories of causation. Int J Epidemiol. 2016 12;45(6):1835–1837. (https://doi.org/10.1093/ije/dyw228)

• Weed DL. Commentary: Causal inference in epidemiology: potential outcomes, pluralism and peer review. Int J Epidemiol. 2016 12;45(6):1838–1840. (https://doi.org/10.1093/ije/dyw229)

• Broadbent A, Vandenbroucke JP, Pearce N. Response: Formalism or pluralism? A reply to commen- taries on ’Causality and causal inference in epidemiology’. Int J Epidemiol. 2016 12;45(6):1841–1851. (https://doi.org/10.1093/ije/dyw298)

• Krieger N, Davey Smith G. Response: FACEing reality: productive tensions between our epidemiological questions, methods and mission. Int J Epidemiol. 2016 12;45(6):1852–1865. (https://doi.org/10.1093/ije/dyw330)

Thursday: Will a “causal architecture” approach help or hurt?

• Keyes KM, Galea S. Commentary: The Limits of Risk Factors Revisited: Is It Time for a Causal Architecture Approach? Epidemiology 2017;28(1):1–5. (https://doi.org/10.1097/EDE.0000000000000578)

• Poole C. Commentary: Some Thoughts on Consequential Epidemiology and Causal Architecture. Epidemiology 2017;28(1):6–11. (https://doi.org/10.1097/EDE.0000000000000577)

• Keyes KM, Galea S. Re: Some Thoughts on Consequential Epidemiology and Causal Architecture. Epidemiology 2017;28(3):e31–e32. (https://doi.org/10.1097/EDE.0000000000000643)

• Poole C. The Author Responds. Epidemiology 2017;28(3):e32–e33. (https://doi.org/10.1097/EDE.0000000000000644)

Week 5: How should we study non-manipulable exposures?

Tuesday: Are non-manipulable exposures causes?

• VanderWeele TJ, Robinson WR. On the causal interpretation of race in regressions adjusting for confounding and mediating variables. Epidemiology 2014 Jul;25(4):473–84. (https://doi.org/10.1097/EDE.0000000000000105)

• Glymour MM, Spiegelman D. Evaluating Public Health Interventions: 5. Causal Inference in Public Health Research-Do Sex, Race, and Biological Factors Cause Health Outcomes? Am J Public Health 2017 Jan;107(1):81–85. (https://doi.org/10.2105/AJPH.2016.303539)

• Kaufman JS. Commentary: Causal Inference for Social Exposures. Annu Rev Public Health 2019 04;40:7–21. (https://doi.org/10.1146/annurev-publhealth-040218-043735)

Thursday: Case study in discrimination in how police use force

• Knox D, Lowe W, Mummolo J. Administrative records mask racially biased policing. American Political Science Review 2020;114(3):619–637. (https://doi.org/10.1017/S0003055420000039)

• Gaebler J, Cai W, Basse G, Shroff R, Goel S, Hill J. Deconstructing claims of post-treatment bias in observational studies of discrimination. arXiv preprint arXiv:200612460. 2020. (https://arxiv.org/abs/2006.12460)

Week 6: Should we try to randomize interventions?

Tuesday: Are RCT’s special?

• Deaton A, Cartwright N. Understanding and misunderstanding randomized controlled trials. Soc Sci Med 2018;210:2–21. (https://doi.org/10.1016/j.socscimed.2017.12.005)

• Dahabreh IJ. Randomization, randomized trials, and analyses using observational data: A commentary on Deaton and Cartwright. Soc Sci Med 2018;210:41–44. (https://doi.org/10.1016/j.socscimed.2018.05.012)

• Imbens G. Understanding and misunderstanding randomized controlled trials: A commentary on Deaton and Cartwright. Soc Sci Med 2018;210:50–52. (https://doi.org/10.1016/j.socscimed.2018.04.028)

• Suzuki E, VanderWeele TJ. Mechanisms and uncertainty in randomized controlled trials: A commentary on Deaton and Cartwright. Soc Sci Med 2018;210:83–85. (https://doi.org/10.1016/j.socscimed.2018.04.023)

• Deaton A, Cartwright N. Reflections on Randomized Control Trials. Soc Sci Med 2018;210:86–90. (https://doi.org/10.1016/j.socscimed.2018.04.046)

Thursday: What if we can’t randomize?

• Lodi S, Phillips A, Lundgren J, Logan R, Sharma S, Cole SR, et al. Effect Estimates in Randomized Trials and Observational Studies: Comparing Apples With Apples. Am J Epidemiol 2019 08;188(8):1569–1577. (https://doi.org/10.1093/aje/kwz100)

• Dahabreh IJ, Robins JM, Hernan MA. Benchmarking Observational Methods by Comparing Randomized Trials and Their Emulations. Epidemiology 2020 Sep;31(5):614–619. (https://doi.org/10.1097/EDE.0000000000001231)

• Westreich D, Edwards JK, Lesko CR, Cole SR, Stuart EA. Target Validity and the Hierarchy of Study Designs. Am J Epidemiol 2019 02;188(2):438–443. (https://doi.org/10.1093/aje/kwy228)

• Haushofer J, Metcalf CJE. Which interventions work best in a pandemic? Science 2020;368(6495):1063–1065. (http://doi.org/10.1126/science.abb6144)

Week 7: Should we be representative?

Tuesday: Should our studies be representative?

• Rothman KJ, Gallacher JEJ, Hatch EE. Why representativeness should be avoided. Int J Epidemiol 2013;42(4):1012-4. (https://doi.org/10.1093/ije/dys223).

• Elwood JM. Commentary: On representativeness. Int J Epidemiol 2013;42(4):1014-5. (https://doi.org/10.1093/ije/dyt101).

• Nohr EA, Olsen J. Commentary: Epidemiologists have debated representativeness for more than 40 years-has the time come to move on? Int J Epidemiol 2013;42(4):1016-7. (https://doi.org/10.1093/ije/dyt102).

• Richiardi L, Pizzi C, Pearce N. Commentary: Representativeness is usually not necessary and often should be avoided. Int J Epidemiol 2013;42(4):1018-22. (https://doi.org/10.1093/ije/dyt103).

• Rothman KJ, Gallacher JEJ, Hatch EE. Rebuttal: When it comes to scientific inference, sometimes a cigar is just a cigar. Int J Epidemiol 2013;42(4):1026–8. (https://doi.org/10.1093/ije/dyt124).

• Stang A Jöckel K-H. Avoidance of representativeness in presence of effect modification. Int J Epidemiol 2014;43:630–1. (URL: https://doi.org/10.1093/ije/dyt263).

• Rothman K, Hatch E, Gallacher J. Representativeness is not helpful in studying heterogeneity of effects across subgroups. Int J Epidemiol 2014;43:633-4. (URL: https://doi.org/10.1093/ije/dyt265).

Thursday: Should our discipline be representative?

• DeVilbiss EA, Weuve J, Fink DS, Morris MD, Arah OA, Radoc JG, et al. Assessing Representation and Perceived Inclusion among Members in the Society for Epidemiologic Research. Am J Epidemiol 2020 Jan. (https://doi.org/10.1093/aje/kwz281)

• Selection of recently published response papers on diversity and inclusion at American Journal of Epidemiology

Week 8: How should we make statistical inferences?

Tuesday: What good are p-values?

• Wasserstein RL, Schirm AL, Lazar NA. Moving to a world beyond “p<0.05”. The American Statistician 2019;73(sup1):1–19. (https://doi.org/10.1080/00031305.2019.1583913).

• McShane BB, Gal D, Gelman A, Robert C, Tackett JL. Abandon statistical significance. The American Statistician 2019;73(sup1):235–245. (https://doi.org/10.1080/00031305.2018.1527253)

Thursday: How might we do better?

• Greenland S, Senn SJ, Rothman KJ, Carlin JB, Poole C, Goodman SN, et al. Statistical tests, P values, confidence intervals, and power: a guide to misinterpretations. Eur J Epidemiol 2016;31(4):337–50.

• Cole SR, Edwards JK, Greenland S. Surprise! Am J Epidemiol 2020 Jul. (https://doi.org/10.1093/aje/kwaa136)

• Rothman KJ. Taken by Surprise. Am J Epidemiol 2020 Jul. (https://doi.org/10.1093/aje/kwaa137)

Week 9: How bad can it be?

Tuesday: How can we quantify our assumptions?

• Lash TL, Fox MP, MacLehose RF, Maldonado G, McCandless LC, Greenland S. Good practices for quantitative bias analysis. Int J Epidemiol 2014;43(6):1969–85.

• Matthay EC, Glymour MM. A Graphical Catalog of Threats to Validity: Linking Social Science with Epidemiology. Epidemiology 2020;31:376–384. (https://doi.org/10.1097/EDE.0000000000001161)

Thursday: E-values, worthwhile or worthless?

• VanderWeele TJ, Ding P. Sensitivity Analysis in Observational Research: Introducing the E-Value. Ann Intern Med 2017;167(4):268–274. (https://doi.org/10.7326/M16-2607)

• Blum MR, Tan YJ, Ioannidis JPA. Use of E-values for addressing confounding in observational studies-an empirical assessment of the literature. Int J Epidemiol. 2020 Jan. (https://doi.org/10.1093/ije/dyz261)

• Groenwold RHH. Commentary: Quantifying the unknown unknowns. Int J Epidemiol 2020 Jun (URL: https://doi.org/10.1093/ije/dyaa092).

• Fox MP, Arah OA, Stuart EA. Commentary: The value of E-values and why they are not enough. Int J Epidemiol. 2020 Jul. (https://doi.org/10.1093/ije/dyaa093)

• Greenland S. Commentary: An argument against E-values for assessing the plausibility that an association could be explained away by residual confounding. Int J Epidemiol. 2020 Aug. (https://doi.org/10.1093/ije/dyaa095)

• VanderWeele TJ, Mathur MB. Commentary: Developing best-practice guidelines for the reporting of E-values. Int J Epidemiol. 2020 Aug. (https://doi.org/10.1093/ije/dyaa094)

Week 10: To whom do epidemiologic results apply?

Tuesday: How should we think about generalizing to other populations?

• Westreich D, Edwards JK, Lesko CR, Cole SR, Stuart EA. Target Validity and the Hierarchy of Study Designs. Am J Epidemiol 2019;188:438–443. (https://doi.org/10.1093/aje/kwy228)

• Lesko CR, Buchanan AL, Westreich D, Edwards JK, Hudgens MG, Cole SR. Generalizing Study Results: A Potential Outcomes Perspective. Epidemiology 2017;28:553–561. (https://doi.org/10.1097/EDE.0000000000000664)

• Dahabreh IJ, Robertson SE, Steingrimsson JA, Stuart EA, Hernan MA. Extending inferences from a randomized trial to a new target population. Stat Med 2020;39(14):1999–2014. (https://doi.org/10.1002/sim.8426)

Thursday: Will “precision” epidemiology help?

• Chowkwanyun M, Bayer R, Galea S. ”Precision” Public Health - Between Novelty and Hype. N Engl J Med 2018 Oct;379(15):1398–1400. (https://doi.org/10.1093/ije/dyy184)

• Taylor-Robinson D, Kee F. Precision public health-the Emperor’s new clothes. Int J Epidemiol 2019 02;48(1):1–6. (https://doi.org/10.1093/ije/dyy184)

• Ladner JT, Grubaugh ND, Pybus OG, Andersen KG. Precision epidemiology for infectious disease control. Nat Med 2019 02;25(2):206–211. (http://doi.org/10.1038/s41591-019-0345-2)

• Rasmussen SA, Khoury MJ, Del Rio C. Precision Public Health as a Key Tool in the COVID-19 Response. JAMA 2020 Aug. (https://doi.org/10.1093/ije/dyy184)

Week 11: Is research (including epidemiology) reliable?

Tuesday: What is the problem?

• Oliver, J. Scientific studies. Last Week Tonight with John Oliver, Season 3, Episode 11, May 5, 2016 (https://www.youtube.com/watch?v=0Rnq1NpHdmw) Note: contains explicit and crude language

• Aschwanden C. Science Isn’t Broken [Internet]. FiveThirtyEight. 2015. Available from: https://fivethirtyeight.com/features/science-isnt-broken/

• Baker M. 1,500 scientists lift the lid on reproducibility. Nature 2016:533(7604):452–4. (https://doi.org/10.1038/533452a)

Thursday: What are some potential solutions?

• Lash TL. The Harm Done to Reproducibility by the Culture of Null Hypothesis Significance Testing. Am J Epidemiol 2017;186: 627-635. (https://doi.org/10.1093/aje/kwx261).

• Munafo MR et al. A manifesto for reproducible science. Nature Human Behaviour 2017;1:1–9. (https://doi.org/10.1038/s41562-016-0021)

• Goodman SN, Fanelli D, Ioannidis JPA. What does research reproducibility mean? Sci Transl Med 2016 06;8(341):341ps12. (https://doi.org/10.1126/scitranslmed.aaf5027)

Week 12: How to put together all of the evidence?

Tuesday: What does “triangulation” mean?

• Lawlor DA, Tilling K, Davey Smith G. Triangulation in aetiological epidemiology. Int J Epidemiol 2016;45(6):1866–86. (https://academic.oup.com/ije/article/45/6/1866/2930550)

• Matthay EC, Hagan E, Gottlieb LM, Tan ML, Vlahov D, Adler NE, et al. Alternative causal inference methods in population health research: Evaluating tradeoffs and triangulating evidence. SSM Popul Health 2020 Apr;10:100526. (https://doi.org/10.1016/j.ssmph.2019.100526)

Thursday: How do we deal with uncertainty?

• Savitz DA, Wellenius GA. Characterization and Communication of Conclusions. In: Interpreting epidemiologic evidence: connecting research to applications. Oxford University Press; 2016. p. 200–10.

• Manski CF. Communicating uncertainty in policy analysis. Proc Natl Acad Sci U S A 2019 04;116(16):7634–7641. (https://doi.org/10.1073/pnas.1722389115)

• Khorana S, Owens K. Understanding medical uncertainty in the hydroxychloroquine debate. Brookings Tech Stream July 23, 2020 (https://www.brookings.edu/techstream/understanding-medical-uncertainty-in-the-hydroxychloroquine-debate/)

Week 13: How should we communicate epidemiologic evidence?

Tuesday: Case study: COVID-19, models, and scientific communication

• Fuller J. Models v. evidence. Boston Review 2020 May 5. (http://bostonreview.net/science-nature/jonathan-fuller-models-v-evidence)

• Lipsitch M. Good science is good science. Boston Review 2020 May 12. (https://bostonreview.net/science-nature/marc-lipsitch-good-science-good-science)

• Ioannidis JPA. The Totality of the Evidence. Boston Review 2020 May 26. (https://bostonreview.net/science-nature/john-p-ioannidis-totality-evidence)

• Lipsitch M. Good science is good science: we need specialists, not sects. Eur J Epidemiol 2020;35(6):519–522. (https://doi.org/10.1007/s10654-020-00651-2)

Thursday: Does “evidence” even matter?

• Lee S. An Elite Group Of Scientists Tried To Warn Trump Against Lockdowns In March. BuzzFeed News July 24, 2020. (https://www.buzzfeednews.com/article/stephaniemlee/ioannidis-trump-white-house-coronavirus-lockdowns).

• Mingle J. Our Lethal Air. New York Review of Books 2019;66(14). (https://www.nybooks.com/articles/2019/09/26/our-lethal-air-pollution/).

Week 14, 2020-12-01: Parting discussion

Tuesday

• Final discussion.
• Course review and feedback.