Alle afleveringen
72 afleveringen, van nieuw naar oud
-
S2E1: Modern Epidemiology: An interview with Dr. Kenneth Rothman
7 september 2021 · 33 min
We are going in a new direction for Season 2 of SERious Epidemiology. This season Hailey and Matt are focusing exclusively on the new fourth edition of the textbook Modern Epidemiology. The textbook has played such an important role in the training of epidemiologists since the first edition was released and has taken on an even larger role within the field as more editions have come out. We will work through each chapter and talk about what key insights we got from them and we will talk to guests about their experiences with the text. In this first episode of the season, we are delighted to present our interview with Dr. Kenneth Rothman, author of the first edition and co-author of editions two through four. Lees meer
Show notes:
Link to Modern Epidemiology:
https://www.amazon.com/Modern-Epidemiology-Kenneth-Rothman/dp/1451193289Link to Epidemiology: An Introduction
https://www.amazon.com/Epidemiology-Introduction-Kenneth-J-Rothman/dp/0199754551/ref=sr_1_1?dchild=1&keywords=Epidemiology%3A+An+Introduction&qid=1630253351&s=books&sr=1-1 -
1.20 Season 1 Finale: Will we ever have to stop wearing sweatpants to work? Lessons from a year of pandemic podcasting.
1 mei 2021 · 39 min
Join Matt Fox and Hailey Banack for our final episode of the first season of SERious Epidemiology, a season which happened to take place entirely during the COVID-19 pandemic. The pandemic has raised countless public health issues for us all to consider from virus testing to health disparities to safe classrooms to vaccine distribution. For the first time (maybe ever), nearly everyone knows what epidemiology is, and we are all hopefully done with having to explain that we are not a group of skin doctors (“we study epidemics… not the epidermis”). In this episode we discuss a few pandemic-related issues particularly relevant for epidemiologists, such as whether we’ll ever have to wear work pants again, the use pre-prints and the value of peer review, and issues related to confirmation bias. Lees meer
-
1.19 SERious Epi Journal Club – BNT162b2 mRNA Covid-19 Vaccine in a Nationwide Mass Vaccination Setting
15 april 2021 · 28 min
In this journal club episode, Dr. Matt Fox and Dr. Hailey Banack discuss a paper recently published in the New England Journal of Medicine by Dagan et al. on the Pfizer COVID-19 vaccine. Listen in for a real-world example of the concept of emulating a target trial and a discussion of how an epidemiologic study can be described as truly beautiful. Lees meer
Reference:
Dagan N, Barda N, Kepten E, Miron O, Perchik S, Katz MA, Hernán MA, Lipsitch M, Reis B, Balicer RD. BNT162b2 mRNA Covid-19 Vaccine in a Nationwide Mass Vaccination Setting. N Engl J Med. 2021 Feb 24:NEJMoa2101765. doi: 10.1056/NEJMoa2101765. Epub ahead of print. PMID: 33626250; PMCID: PMC7944975. -
1.18 Lifecourse epidemiology: a melting pot of bias?
1 april 2021 · 36 min
The topic of this episode is lifecourse epidemiology, defined by Dr. Paola Gilsanz as the biological, behavioural and social processes that influence an individual’s health outcomes throughout their life. Join us as we discuss models commonly used in lifecourse epidemiology, such as the early life critical period model, accumulation model, and pathway model. Is lifecourse epidemiology different than social epidemiology? Is all epidemiology lifecourse epidemiology because we study individuals at some point in their lifetime? Dr. Gilsanz answers these questions for us and also highlights the importance of using different data sources depending on your question of interest and the specific types of bias that are particularly prevalent in lifecourse epidemiology. Lees meer
Show notes:
Brazilian cheese bread recipe:
https://braziliankitchenabroad.com/brazilian-cheese-bread/ -
1.17 Do external validity and transportability confuse the daylights out of you?
15 maart 2021 · 46 min
Ask yourself these true or false questions: Lees meer
Generalizability and transportability and external validity are all the same thing
Generalizability is a secondary concern to internal validity
We spend too much time in epi training programs teaching internal validity and not enough teaching external validity
Worrying about external validity is largely and academic exercise that doesn’t really have much in the way of real-world impact.In this episode of SERious Epi we discuss these questions and more with Dr. Megha Mehrotra. While internal and external validity are familiar to nearly all epidemiologists, the concept of transportability is less familiar. Listen in to this episode for a clear description of how concepts related to validity, generalizability, and transportability are similar, and different, from each other.
-
1.16 Finding the Perfect Match Requires Common Support: Matching with Dr. Anusha Vable
1 maart 2021 · 45 min
Matching is something we learn about in our intro to epidemiology classes and yet we probably spend little time thinking about it after that, we just do it. But when should we match and when does it help us and when does it hurt us? What do we need to consider before we match? Dr. Anusha Vable joins us to help us understand matching in detail. Lees meer
For those of you looking to do more reading around matching see:
Ho, D., Imai, K., King, G., & Stuart, E. (2007). Matching as Nonparametric Preprocessing for Reducing Model Dependence in Parametric Causal Inference. Political Analysis, 15(3), 199-236. doi:10.1093/pan/mpl013
Stuart EA. Matching methods for causal inference: A review and a look forward. Stat Sci. 2010 Feb 1;25(1):1-21. doi: 10.1214/09-STS313. PMID: 20871802; PMCID: PMC2943670.
Vable AM, Kiang MV, Glymour MM, Rigdon J, Drabo EF, Basu S. Performance of Matching Methods as Compared With Unmatched Ordinary Least Squares Regression Under Constant Effects. Am J Epidemiol. 2019 Jul 1;188(7):1345-1354. doi: 10.1093/aje/kwz093. PMID: 30995301; PMCID: PMC6601529.
Iacus, S., King, G., & Porro, G. (2012). Causal Inference without Balance Checking: Coarsened Exact Matching. Political Analysis, 20(1), 1-24. doi:10.1093/pan/mpr013 -
1.15 The pool is big enough for all of us: Representativeness with Dr. Jonathan Jackson
15 februari 2021 · 45 min
Perhaps the biggest challenge we all face in epidemiologic research is recruitment of study participants. And recruiting a diverse population for our studies that allows for broad generalizability and transportability of effect estimates is something we haven’t done a good enough job of and as a consequence, our work has suffered. While we may think of this as not a methods issue, Dr. Jonathan Jackson helps us understand why representativeness affects or work and how we can do better. Lees meer
-
1.14. It’s always a competition: Competing Risks with Dr. Bryan Lau
1 februari 2021 · 43 min
Do you, like us, understand that competing risks are important to account for and yet are not 100% sure exactly what they are and when they matter? Do you stay up at night wondering if competing risks regressions are necessary for valid inference in your study? If so, this episode is for you. Dr. Bryan Lau gives us the details on this important method. Lees meer
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:
Koller MT, Raatz H, Steyerberg EW, Wolbers M. Competing risks and the clinical community: irrelevance or ignorance? Stat Med. 2012 May 20;31(11-12):1089-97.
Andersen PK, Geskus RB, de Witte T, Putter H. Competing risks in epidemiology: possibilities and pitfalls. Int J Epidemiol. 2012 Jun;41(3):861-70.
Allignol A, Schumacher M, Wanner C, Drechsler C, Beyersmann J. Understanding competing risks: a simulation point of view. BMC Med Res Methodol. 2011 Jun 3;11:86.
Grambauer N, Schumacher M, Dettenkofer M, Beyersmann J. Incidence densities in a competing events analysis. Am J Epidemiol. 2010 Nov 1;172(9):1077-84.
Lau B, Cole SR, Gange SJ. Competing risk regression models for epidemiologic data. Am J Epidemiol. 2009 Jul 15;170(2):244-56. -
1.13. It’s all about the instruments: with Sonja Swanson
15 januari 2021 · 49 min
What are instrumental variables? Should I be using them in my research? And if so, how do I do that? In this episode of SERious Epidemiology, we talk with Dr. Sonja Swanson about what instrumental variables are and what’s so great (and not so great) about them. Lees meer
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:
Greenland S. An introduction to instrumental variables for epidemiologists. Int J Epidemiol. 2018;47(1):358.
Swanson SA, Labrecque J, Hernán MA. Causal null hypotheses of sustained treatment strategies: What can be tested with an instrumental variable? Eur J Epidemiol. 2018;33(8):723-728.
Brookhart MA, Wang PS, Solomon DH, Schneeweiss S. Instrumental variable analysis of secondary pharmacoepidemiologic data. Epidemiology. 2006;17(4):373-4.
Hernán MA, Robins JM. Instruments for causal inference: an epidemiologist's dream? Epidemiology. 2006;17(4):360-72.
Swanson SA, Hernán MA. Commentary: how to report instrumental variable analyses (suggestions welcome). Epidemiology. 2013;24(3):370-4. -
1.12. Epidemiology podcast crossover
1 januari 2021 · 52 min
In honor of the Society for Epidemiologic Research 2020 Meeting, the hosts of four epidemiology podcasts came together to record the first ever “crossover event” to talk about their experiences recording our shows and what podcasting can bring to the table for the field of epidemiology. Join the hosts of Epidemiology Counts (Bryan James), SERiousEPi (Matt Fox, Hailey Banack), Casual Inference (Lucy D’Agostino McGowan), and Shiny Epi People (Lisa Bodnar) as they engage in a fun and informative (we hope!) conversation of the burgeoning field of epidemiology podcasting, emceed by Geetika Kalloo. The audio podcast will be released on some of our pod feeds, and the video recording will be available to watch on the SER website. Lees meer
-
1.11. The need for theory in epidemiology – with Dr. Nancy Krieger
14 december 2020 · 40 min
Episode Title: The need for theory in epidemiology with Dr. Nancy Krieger Lees meer
This episode features an interview with Dr. Nancy Krieger, Professor of Social Epidemiology at the T.H. Chan School of Public Health and author of Epidemiology and the People’s Health: Theory and Context. Dr. Krieger discusses the importance of using conceptual frameworks to improve people’s health and the role of population-level determinants of health (including social determinants) in population health research. We discuss a range of topics, including the differences between biomedical and analytics driven approaches to population health research and theory driven research, as well as the importance of descriptive epidemiology.
-
1.10. Quasi-experimental Studies – A Love Story: With Tarik Benmarhnia
1 december 2020 · 52 min
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 Lees meer
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, Tan…
-
1.9. When Epidemiologists and Variables Collide: with Elizabeth Rose Mayeda
15 november 2020 · 43 min
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. Lees meer
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.
-
1.8. The Discipline Olympics: Epidemiology vs. Public Health with Dr. Laura Rosella
1 november 2020 · 51 min
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. Lees meer
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.caDefinitions 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.0208442Terris, 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%2DThe 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-met… -
1.7. The Bread and Butter of Bayes with Ghassan Hamra
15 oktober 2020 · 46 min
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). Lees meer
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://do…
-
1.6. Questioning the Questions with Maria Glymour
1 oktober 2020 · 44 min
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. Lees meer
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
-
1.5. Putting the Social Back in Social Epidemiology with Dr. Whitney Robinson
15 september 2020 · 41 min
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. Lees meer
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 -
1.4. Statisticalize your intervention soup: A journal club episode discussing Hernan and Taubman’s “Does obesity shorten life?”
1 september 2020 · 53 min
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. Lees meer
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.
-
1.3. The Countercultural Counterfactual Episode with Dr. Daniel Westreich
17 augustus 2020 · 53 min
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. Lees meer
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 Assoc…
-
Bonus Episode 1.2.5: “Making Causal Inference More Social and (Social) Epidemiology More Causal” with Dr. Onyebuchi Arah and Dr. John W. Jackson
3 augustus 2020 · 56 min
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. Lees meer
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 -
1.2. The Time is Not on Your Side Episode with Dr. Ellie Murray
15 juli 2020 · 52 min
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. Lees meer
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
-
1.1. SERious EPI – Introduction
26 juni 2020 · 11 min
Do you want to know more about novel methods in epidemiology but don’t have the time read a bunch of papers on the topic? Do you want to keep current on the latest developments but can’t go back to school for another degree? Do you just want the big picture understanding so you can follow along? SERious EPI is a new podcast from the Society for Epidemiologic Research hosted by Hailey Banack and Matt Fox. The podcast will include interviews with leading epidemiology researcher who are experts on cutting edge and novel methods. Interviews will focus on why these methods are so important, what problems they solve, and how they are currently being used. The podcast is targeted towards current students as well as practicing epidemiologists who want to learn more from experts in the field. Lees meer