SERious EPI

What puts the quasi in quasi-experimental designs? What makes a quasi-experimental study different than a “real” experiment? Ever wondered about the difference between regression discontinuity, difference-in-differences, and synthetic control methods? Dr. Tarik Benmarnhia joins us on this episode of SERious Epidemiology to talk us through a range of quasi-experimental designs. He makes a strong case for why we should integrate these designs in a variety of settings in epidemiology ranging from public health policy to clinical epidemiology

After listening to this podcast, if you are interested in learning more about quasi-experimental designs, you can check out some of the resources below:

Abadie A, Diamond A, Hainmueller J. (2010) Synthetic Control Methods for Comparative Case Studies: Estimating the Effect of California’s Tobacco Control Program, Journal of the American Statistical Association, 105:490, 493-505, DOI: 10.1198/jasa.2009.ap08746

Chen H, Li Q, Kaufman JS, Wang J, Copes R, Su Y, Benmarhnia T. Effect of air quality alerts on human health: a regression discontinuity analysis in Toronto, Canada. Lancet Planet Health. 2018 Jan;2(1):e19-e26. doi: 10.1016/S2542-5196(17)30185-7. Epub 2018 Jan 9. PMID: 29615204.

Auger N, Kuehne E, Goneau M, Daniel M. Preterm birth during an extreme weather event in Québec, Canada: a "natural experiment". Matern Child Health J. 2011 Oct;15(7):1088-96. doi: 10.1007/s10995-010-0645-0. PMID: 20640493.

Hernán MA, Robins JM. Instruments for causal inference: an epidemiologist's dream? Epidemiology. 2006 Jul;17(4):360-72. doi: 10.1097/01.ede.0000222409.00878.37. Erratum in: Epidemiology. 2014 Jan;25(1):164. PMID: 16755261.

Courtemanche, C., Marton, J., Ukert, B., Yelowitz, A. and Zapata, D. (2017), Early Impacts of the Affordable Care Act on Health Insurance Coverage in Medicaid Expansion and Non‐Expansion States. J. Pol. Anal. Manage., 36: 178-210. https://doi.org/10.1002/pam.21961

Bor J, Fox MP, Rosen S, Venkataramani A, Tanser F, Pillay D, Bärnighausen T. Treatment eligibility and retention in clinical HIV care: A regression discontinuity study in South Africa. PLoS Med. 2017 Nov 28;14(11):e1002463. doi: 10.1371/journal.pmed.1002463. PMID: 29182641; PMCID: PMC5705070.

Bor J, Moscoe E, Mutevedzi P, Newell ML, Bärnighausen T. Regression discontinuity designs in epidemiology: causal inference without randomized trials. Epidemiology. 2014 Sep;25(5):729-37. doi: 10.1097/EDE.0000000000000138. PMID: 25061922; PMCID: PMC4162343.

Elder TE. The importance of relative standards in ADHD diagnoses: evidence based on exact birth dates. J Health Econ. 2010;29(5):641-656. doi:10.1016/j.jhealeco.2010.06.003

Smith LM, Kaufman JS, Strumpf EC, Lévesque LE. Effect of human papillomavirus (HPV) vaccination on clinical indicators of sexual behaviour among adolescent girls: the Ontario Grade 8 HPV Vaccine Cohort Study. CMAJ. 2015;187(2):E74-E81. doi:10.1503/cmaj.140900

In most introductory epidemiology courses, students are taught about three categories of bias: confounding, information bias, and selection bias. On this episode of the podcast, we talk to Dr. Elizabeth Rose Mayeda about where collider stratification bias fits in to the framework of biases in epidemiology. Is collider stratification bias the same as selection bias? Why is collider bias so hard to understand, conceptually and empirically? Does collider stratification bias even matter? Listen in for some great conversation explaining these topics and others.

After listening to this podcast, if you are interested in learning more about selection bias and collider stratification bias some resources are included below:

Hernán MA, Hernández-Díaz S, Robins JM. A structural approach to selection bias. Epidemiology. 2004;15:615-625.

Howe CJ, Cole SR, Lau B, Napravnik S, Eron JJJ. Selection Bias Due to Loss to Follow Up in Cohort Studies. Epidemiology. 2016;27:91-97.

Hernán MA. Invited Commentary: Selection Bias Without Colliders. American journal of epidemiology. 2017;185:1048-1050.

Greenland S. Response and follow-up bias in cohort studies. Am J Epidemiol. 1977 Sep;106(3):184-7. doi: 10.1093/oxfordjournals.aje.a112451.

Kleinbaum D, Morgenstern H, Kupper L. Selection bias in epidemiologic studies. Am J Epidemiol. 1981;113:452-463.

Greenland S, Pearl J, Robins JM. Causal Diagrams for Epidemiologic Research. Epidemiology. 1999;10:37-48.

Mayeda ER, Banack HR, Bibbins-Domingo K, Zeki Al Hazzouri A, Marden JR, Whitmer RA, et al. Can Survival Bias Explain the Age Attenuation of Racial Inequalities in Stroke Incidence?: A Simulation Study. Epidemiology. 2018;29:525-532.

Given the COVID-19 pandemic there is an urgent need for us to better understand how scientific evidence generated in epidemiologic research gets translated into information that can be used to create public health policy. In this episode of SERious Epidemiology, we talk with Dr. Laura Rosella about data driven public health, the role of epidemiology in public health, and more broadly, the importance of knowledge translation for epidemiologists.

After listening to this podcast, if you are interested in learning more about the intersection of epidemiology and public health some resources are included below:

How’s my flattening: A centralized data analytics and visualization hub monitoring Ontario's response to COVID-19

Link: howsmyflattening.ca

Definitions of epidemiology, including references to the definition Dr. Rosella mentioned from McMahon and Pugh’s epidemiology textbook (1970):

Frérot M, Lefebvre A, Aho S, Callier P, Astruc K, Aho Glélé LS. What is epidemiology? Changing definitions of epidemiology 1978-2017. PLoS One. 2018;13(12):e0208442.

doi:10.1371/journal.pone.0208442

Terris, M. Approaches to an Epidemiology of Health. Am J Public Health. 1975; 65(10)

https://ajph.aphapublications.org/doi/pdf/10.2105/AJPH.65.10.1037#:~:text=Introduction,.%221I%20This%20definition%20repre%2D

The use of scientific evidence for public health decision making:

Rosella LC, Wilson K, Crowcroft NS, Chu A, Upshur R, Willison D, Deeks SL, Schwartz B, Tustin J, Sider D, Goel V. Pandemic H1N1 in Canada and the use of evidence in developing public health policies--a policy analysis. Soc Sci Med. 2013 Apr;83:1-9.

doi: 10.1016/j.socscimed.2013.02.009.

Agent-based modeling

Tracy M, Cerdá M, Keyes KM. Agent-Based Modeling in Public Health: Current Applications and Future Directions. Annu Rev Public Health. 2018 Apr 1;39:77-94.

doi: 10.1146/annurev-publhealth-040617-014317.

Additional info on agent-based modeling:

https://www.publichealth.columbia.edu/research/population-health-methods/agent-based-modeling

In this episode we interview Dr. Ghassan Hamra and talk about all things Bayesian. If you’re like us, you have likely been trained in traditional, frequentist approaches to statistics and have always wondered what the big deal is about Bayesian approaches. Well, have no fear, Dr. Hamra is here to explain it all. In this episode we cover a range of topics introducing Bayesian analyses, including how Bayesian and frequentist statistics differ, the concept of integrating a prior into your analyses, and whether Bayesian statistics are really a “subjective” approach (**spoiler alert: they’re not).

After listening to this podcast, if you’re interested in learning more about Bayesian analyses some links are included below:

MacLehose, R.F., Hamra, G.B. Applications of Bayesian Methods to Epidemiologic Research. Curr Epidemiol Rep 1, 103–109 (2014).

https://doi.org/10.1007/s40471-014-0019-z

Hamra GB, MacLehose RF, Cole SR. Sensitivity analyses for sparse-data problems-using weakly informative bayesian priors. Epidemiology. 2013;24(2):233-239.

doi:10.1097/EDE.0b013e318280db1d

Website with links to Dr. Hamra’s publications and presentations/tutorials:

http://ghassanbhamra-phd.org/publications

http://ghassanbhamra-phd.org/presentations-and-such

Series of articles by Sander Greenland on Bayesian methods for epidemiology:

Sander Greenland, Bayesian perspectives for epidemiological research: I. Foundations and basic methods, International Journal of Epidemiology, Volume 35, Issue 3, June 2006, Pages 765–775, https://doi.org/10.1093/ije/dyi312

Sander Greenland, Bayesian perspectives for epidemiological research. II. Regression analysis, International Journal of Epidemiology, Volume 36, Issue 1, February 2007, Pages 195–202, https://doi.org/10.1093/ije/dyl289

Sander Greenland, Bayesian perspectives for epidemiologic research: III. Bias analysis via missing-data methods, International Journal of Epidemiology, Volume 38, Issue 6, December 2009, Pages 1662–1673, https://doi.org/10.1093/ije/dyp278

MacLehose RF, Gustafson P. Is probabilistic bias analysis approximately Bayesian?. Epidemiology. 2012;23(1):151-158. doi:10.1097/EDE.0b013e31823b539c

Why is it so important to ask good study questions? Why is it so hard to develop good study questions? Do all study questions need to be directly relevant for public health policy?  In this episode of SERious Epidemiology, we talk with Dr. Maria Glymour about what it means to ask a good study question and how we can get better at asking questions that will make a meaningful contribution to public health.

After listening to this podcast, if you’re interested in learning more about some of the topics we discussed, here are links for you to check out:

David U. Himmelstein, Robert M. Lawless, Deborah Thorne, Pamela Foohey, and Steffie Woolhandler, 2019. Medical Bankruptcy: Still Common Despite the Affordable Care Act American Journal of Public Health 109, 431_433.

https://doi.org/10.2105/AJPH.2018.304901

Hernán MA, Alonso A, Logan R, et al. Observational studies analyzed like randomized experiments: an application to postmenopausal hormone therapy and coronary heart disease. Epidemiology. 2008;19(6):766-779.

doi:10.1097/EDE.0b013e3181875e61

Maria Glymour and Rita Hamad, 2018. Causal Thinking as a Critical Tool for Eliminating Social Inequalities in Health. American Journal of Public Health 108, 623_623.

https://doi.org/10.2105/AJPH.2018.304383

Harper S, Strumpf EC. Social epidemiology: questionable answers and answerable questions. Epidemiology. 2012 Nov;23(6):795-8.

doi: 10.1097/EDE.0b013e31826d078d.

Sandro Galea, An Argument for a Consequentialist Epidemiology, American Journal of Epidemiology, Volume 178, Issue 8, 15 October 2013, Pages 1185–1191,

https://doi.org/10.1093/aje/kwt172

Is all epidemiology social epidemiology? If I am someone who studies cancer, or obesity, or infectious disease, or any other branch of epidemiology, should I be considering topics related to social epidemiology in my own work? In this episode of SERious Epidemiology, Dr. Whitney Robinson joins us to explain key concepts in social epidemiology.

After listening to this podcast, if you are interested in learning more about social epidemiology or some of the resources mentioned are included below:

Kaufman, J.S. & Oakes, M. Methods in Social Epidemiology, 2nd edition.

https://www.amazon.com/Methods-Social-Epidemiology-Public-Biostatistics/dp/111850559X

Link, Bruce G., and Jo Phelan. “Social Conditions As Fundamental Causes of Disease.” Journal of Health and Social Behavior, 1995, pp. 80–94. JSTOR, www.jstor.org/stable/2626958.

Chandra Ford’s work on critical race praxis:

Ford, Chandra L, and Collins O Airhihenbuwa. “Critical Race Theory, race equity, and public health: toward antiracism praxis.” American journal of public health vol. 100 Suppl 1,Suppl 1 (2010): S30-5. doi:10.2105/AJPH.2009.171058

Ford CL, Airhihenbuwa CO. The public health critical race methodology: Praxis for antiracism research. Social Science & Medicine. 2010;71:1390-1398.

VanderWeele TJ, Robinson WR. On the causal interpretation of race in regressions adjusting for confounding and mediating variables. Epidemiology. 2014;25(4):473-484. doi:10.1097/EDE.0000000000000105

VanderWeele TJ, Robinson WR. Rejoinder: how to reduce racial disparities?: Upon what to intervene?. Epidemiology. 2014;25(4):491-493. doi:10.1097/EDE.0000000000000124

Whitney R Robinson, Zinzi D Bailey, Invited Commentary: What Social Epidemiology Brings to the Table—Reconciling Social Epidemiology and Causal Inference, American Journal of Epidemiology, Volume 189, Issue 3, March 2020, Pages 171–174, https://doi.org/10.1093/aje/kwz197

In this journal club episode, we discuss one of our top 10 favourite epidemiology papers: “Does obesity shorten life? The importance of well-defined interventions to answer causal questions” by Miguel Hernán and Sarah Taubman. We talk about the consistency assumption in causal inference, why we think measurement error needs to be added to the list of assumptions for causal inference, and invent a new word (“statisticalize”) to dismiss the notion that fancy methods can always solve our problems.

 

References:

Hernán MA, Taubman SL. Does obesity shorten life? The importance of well-defined interventions to answer causal questions. Int J Obes. 2008;32:s8-s14.

Cole S, Frangakis C. The consistency statement in causal inference: a definition or an assumption? Epidemiology. 2009; 20:3-5.

Causal inference and the potential outcomes model are now both commonly taught in graduate programs in epidemiology. However, I think we can all agree that counterfactual thinking can be a bit mind-bending at times and it is really easy to get lost deep in the weeds when trying to think through the potential for unobserved comparison groups or outcomes. In this episode of SERious Epi, we speak to Dr. Daniel Westreich about counterfactuals, the difference between causal inference and causal effect estimation, and assumptions required to estimate causal effects from observational data.

 

After listening to this podcast, if you’re interested in learning more about the potential outcomes model or checking out any of the resources mentioned on this podcast, links are included below:

- Rose, G. Sick individuals and sick populations. International Journal of Epidemiology 1985; 14:32–38.

- Greenland, S. For and Against Methodologies: Some Perspectives on Recent Causal and Statistical Inference Debates. Eur J Epidemiol 32, 3–20 (2017). https://doi.org/10.1007/s10654-017-0230-6

- Morabia, Alfredo. "On the Origin of Hill's Causal Criteria." Epidemiology 2, no. 5 (1991): 367-69. Accessed August 13, 2020. www.jstor.org/stable/20065702.

- Hernán MA, Robins JM (2020). Causal Inference: What If. Boca Raton: Chapman & Hall/CRC.

-Westreich, D. (2020). Epidemiology by Design: A Causal Approach to the Health Sciences. https://global.oup.com/academic/product/epidemiology-by-design-9780190665760?cc=us&lang=en&

- Hernán MA, Hernández-Díaz S, Robins JM. A structural approach to selection bias. Epidemiology. 2004;15:615-625.

- Hernán MA, Taubman SL. Does obesity shorten life? The importance of well-defined interventions to answer causal questions. Int J Obes. 2008;32:s8-s14.

-Neyman, J (1923) http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.473.4367&rep=rep1&type=pdf

- Donald B Rubin (2005) Causal Inference Using Potential Outcomes, Journal of the American Statistical Association, 100:469, 322-331, DOI: 10.1198/016214504000001880

- Edwards JK, Cole SR, Westreich D. All your data are always missing: incorporating bias due to measurement error into the potential outcomes framework. Int J Epidemiol. 2015;44(4):1452-1459. doi:10.1093/ije/dyu272

- Westreich D, Edwards JK, Cole SR, Platt RW, Mumford SL, Schisterman EF. Imputation approaches for potential outcomes in causal inference. Int J Epidemiol. 2015;44(5):1731-1737. doi:10.1093/ije/dyv135

At SER 2019, the Cassel lecture was delivered by Miguel Hernán and Sandro Galea on the topic of  reconciling social epidemiology and causal inference. Their talk was turned into a paper in the American Journal of Epidemiology, and in March 2020, was published along with a series of responses by Drs. Enrique Schisterman, Whitney Robinson and Zinzi Bailey, Tyler VanderWeele, and John Jackson and Onyebuchi Arah.  In this SERious Epi bonus journal club episode, we had conversation with Dr. John Jackson and Dr. Onyebuchi Arah about their commentary and had the opportunity to ask their thoughts on the other topics published in that issue.

Links:

Win-Win: Reconciling Social Epidemiology and Causal Inference by Sandro Galea and Miguel A Hernán

https://doi.org/10.1093/aje/kwz158

Editorial: Let’s Be Causally Social by Enrique F Schisterman

https://doi.org/10.1093/aje/kwz237

Invited Commentary: What Social Epidemiology Brings to the Table—Reconciling Social Epidemiology and Causal Inference by Whitney R Robinson, Zinzi D Bailey

https://doi.org/10.1093/aje/kwz197

Invited Commentary: Counterfactuals in Social Epidemiology—Thinking Outside of “the Box”

https://doi.org/10.1093/aje/kwz198

Invited Commentary: Making Causal Inference More Social and (Social) Epidemiology More Causal by John W Jackson and Onyebuchi A Arah

https://doi.org/10.1093/aje/kwz199

Galea and Hernán Respond to “Brings to the Table,” “Differential Measurement Error,” and “Causal Inference in Social Epidemiology”

https://doi.org/10.1093/aje/kwz201

Have you ever wondered why it is so important to consider the concept of time in epidemiologic analyses? And, more importantly, what strategies exist to appropriately account for time and time-varying variables? Time dependent confounding? In the first-ever episode of SERious Epidemiology, Dr. Eleanor Murray will be discussing the concept of time in epidemiologic research and explaining different types of time-related bias.

After listening to this podcast, if you’re interested in learning more about time or checking out any of the resources mentioned on this podcast, links are included below:

Young, J.G., Vatsa, R., Murray, E.J. et al. Interval-cohort designs and bias in the estimation of per-protocol effects: a simulation study. Trials 20, 552 (2019).

https://trialsjournal.biomedcentral.com/articles/10.1186/s13063-019-3577-z

Weuve J, Tchetgen Tchetgen EJ, Glymour MM, et al. Accounting for bias due to selective attrition: the example of smoking and cognitive decline. Epidemiology. 2012;23(1):119-128.

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3237815/

Hernán MA, Robins JM (2020). Causal Inference: What If. Boca Raton: Chapman & Hall/CRC.

https://www.hsph.harvard.edu/miguel-hernan/causal-inference-book/

Society for Epidemiologic Research 2019 Annual Meeting Symposium Presentation

“The Baddest of the Bad: Ranking the Most Pernicious Biases Facing Observational Studies”

Catherine Lesko, Matthew Fox, Robert Platt, Maria Glymour, Jessie Edwards, Ashley Naimi, Chanelle Howe, Jay Kaufman

https://epiresearch.org/2019/06/21/the-baddest-of-the-bad-ranking-the-most-pernicious-biases-facing-observational-studies/

 

For anyone interested in learning more specifically about immortal time bias, this paper is a terrific introduction:

Lévesque LE, Hanley JA, Kezouh A, Suissa S. Problem of immortal time bias in cohort studies: example using statins for preventing progression of diabetes. BMJ. 2010;340:b5087.

https://www.bmj.com/content/340/bmj.b5087.long