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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:

1. 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

2. 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

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

http://ghassanbhamra-phd.org/publications

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

4. 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

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

Podcast: Play in new window | Download (Duration: 44:13 — 40.5MB)

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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:

1. 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

2. 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

3. 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

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

doi: 10.1097/EDE.0b013e31826d078d.

5. 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