Podcast: Play in new window | Download (Duration: 36:19 — 33.2MB)

Subscribe: Spotify | TuneIn | RSS | More

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.

Show notes:
Brazilian cheese bread recipe:
https://braziliankitchenabroad.com/brazilian-cheese-bread/

Podcast: Play in new window | Download (Duration: 45:45 — 41.9MB)

Subscribe: Spotify | TuneIn | RSS | More

Ask yourself these true or false questions:

1. Generalizability and transportability and external validity are all the same thing
2. Generalizability is a secondary concern to internal validity
3. We spend too much time in epi training programs teaching internal validity and not enough teaching external validity
4. 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.

Podcast: Play in new window | Download (Duration: 44:45 — 41.0MB)

Subscribe: Spotify | TuneIn | RSS | More

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.

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

Podcast: Play in new window | Download (Duration: 44:44 — 41.0MB)

Subscribe: Spotify | TuneIn | RSS | More

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.

Podcast: Play in new window | Download (Duration: 43:09 — 39.5MB)

Subscribe: Spotify | TuneIn | RSS | More

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.

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:

1. 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.
2. 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.
3. 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.
4. 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.
5. Lau B, Cole SR, Gange SJ. Competing risk regression models for epidemiologic data. Am J Epidemiol. 2009 Jul 15;170(2):244-56.

Podcast: Play in new window | Download (Duration: 49:17 — 45.1MB)

Subscribe: Spotify | TuneIn | RSS | More

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.

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:

1. Greenland S. An introduction to instrumental variables for epidemiologists. Int J Epidemiol. 2018;47(1):358.
2. 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.
3. Brookhart MA, Wang PS, Solomon DH, Schneeweiss S. Instrumental variable analysis of secondary pharmacoepidemiologic data. Epidemiology. 2006;17(4):373-4.
4. Hernán MA, Robins JM. Instruments for causal inference: an epidemiologist’s dream? Epidemiology. 2006;17(4):360-72.
5. Swanson SA, Hernán MA. Commentary: how to report instrumental variable analyses (suggestions welcome). Epidemiology. 2013;24(3):370-4.

Podcast: Play in new window | Download (Duration: 52:25 — 48.0MB)

Subscribe: Spotify | TuneIn | RSS | More

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.

Podcast: Play in new window | Download (Duration: 40:10 — 36.8MB)

Subscribe: Spotify | TuneIn | RSS | More

Episode Title: The need for theory in epidemiology with Dr. Nancy Krieger

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.

Podcast: Play in new window | Download (Duration: 51:59 — 47.6MB)

Subscribe: Spotify | TuneIn | RSS | More

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

Podcast: Play in new window | Download (Duration: 43:22 — 39.7MB)

Subscribe: Spotify | TuneIn | RSS | More

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.