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In this episode of Season 2 of SERious Epidemiology, (recorded back when we were getting COVID booster shots) Hailey and Matt connect with Dr. Ellie Matthay for a discussion on Chapter 8 on case-control studies. We finally answer whether it is spelled with a – or not (and Hailey and Ellie disagree with Matt about semicolons). We discuss how cohort studies and case control studies differ and overlap. We talk about whether case control studies are more biased than cohort studies. And Hailey reveals her dreams for releasing Modern Epidemiology: the Audiobook (with possible singing).

Podcast: Play in new window | Download (Duration: 44:20 — 60.9MB)

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In this episode of Season 2 of SERious Epidemiology, Hailey and Matt get into the humble case control study. We discuss the ins and outs of this much maligned study design that has so flummoxed so many in epidemiology. We ask the hard questions about the best way sample in a case control study, whether we spend too much or not enough time on it in our teaching, whether a case control study always has to be nested within some hypothetical cohort, whether the design is inherently more biased than cohort studies (spoiler: no, but…), why some people refer to cases and controls when they are not referring to a case control study, and, if it were on a famous TV show, which character the case control study would be (and more importantly, why Hailey has never seen said TV show).

Papers referenced in this episode:

Selection of Controls in Case-Control Studies: I. Principles
Sholom Wacholder, Joseph K. McLaughlin, Debra T. Silverman, Jack S. Mandel
American Journal of Epidemiology, Volume 135, Issue 9, 1 May 1992, Pages 1019–1028, https://doi.org/10.1093/oxfordjournals.aje.a116396

Selection of Controls in Case-Control Studies: II. Types of Controls
Sholom Wacholder, Debra T. Silverman, Joseph K. McLaughlin, Jack S. Mandel
American Journal of Epidemiology, Volume 135, Issue 9, 1 May 1992, Pages 1029–1041, https://doi.org/10.1093/oxfordjournals.aje.a116397

Selection of controls in case-control studies. III. Design options
S Wacholder ¹, D T Silverman, J K McLaughlin, J S Mandel
Wacholder S, Silverman DT, McLaughlin JK, Mandel JS. Selection of controls in case-control studies. III. Design options. Am J Epidemiol. 1992 May 1;135(9):1042-50.
doi: 10.1093/oxfordjournals.aje.a116398

Podcast: Play in new window | Download (Duration: 53:30 — 73.5MB)

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In this episode of Season 2 of SERious Epidemiology, Hailey and Matt get some real world experience with cohort studies in a conversation with Dr. Vasan Ramachandran, PI of the Framingham Heart Study (FHS). FHS is a very well-known cohort study and the model that many of us have in mind when we think of cohort studies. We get a bit of history on FHS and Hailey and I have a chance to ask the questions we have struggled with around cohort studies including the role of representativeness. And, spoiler alert, we learn that FHS did not invent the term “risk factor” as Matt has been telling his students for years.

Podcast: Play in new window | Download (Duration: 47:57 — 65.9MB)

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In this episode of Season 2 of SERious Epidemiology, Hailey and Matt get into cohort studies. We spend a lot of time confessing our limitations, both personally, and as a field, in assigning person time. We talk about the end of the large cohort study and the challenges in determining when to consider a person as exposed. We talk about issues of immortal person time and whether it is technically acceptable to include those who already have the outcome in a cohort study.

Podcast: Play in new window | Download (Duration: 58:14 — 80.0MB)

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In this episode of Season 2 of SERious Epidemiology, Hailey and Matt connect with Dr. Katie Lesko for a discussion on Chapter 5 on measures of association and measures of effect. We confess our challenge with working with person time. We talk about the importance of a well specified time zero. We talk about why epidemiology is complicated by free will. We ponder what the counterfactual model looks like with time to event models. We talk about the challenges of real world data vs idealized studies. We discuss the challenges of interpreting effect measure modification. And we learn that Katie was a rower in college and is concerned that her daughter may never win an Olympic medal in gymnastics.

A few papers that are mentioned in the episode:

Hernán MA. Invited Commentary: Selection Bias Without Colliders. Am J Epidemiol. 2017 Jun 1;185(11):1048-1050. doi: 10.1093/aje/kwx077. PMID: 28535177; PMCID: PMC6664806.

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 Aug;44(4):1452-9. doi: 10.1093/ije/dyu272. Epub 2015 Apr 28. PMID: 25921223; PMCID: PMC4723683.

Cole SR, Hudgens MG, Brookhart MA, Westreich D. Risk. Am J Epidemiol. 2015 Feb 15;181(4):246-50. doi: 10.1093/aje/kwv001. Epub 2015 Feb 5. PMID: 25660080; PMCID: PMC4325680.

Podcast: Play in new window | Download (Duration: 53:11 — 73.0MB)

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In this episode of Season 2 of SERious Epidemiology, Hailey and Matt record, then re-record due to a technical error (ooops!) a discussion on Chapter 5 on measures of association and measures of effect. We say whether we prefer risks or rates. We talk about the counterfactual, causal contrasts, valid inferences and good comparison groups. We use the phrase “living your best epi life”. And we define the difference between associations and effects. We answer whether smoking cessation programs increase the risk of being hit by a drunk driver (and if so, whether that’s causal). There is a mystery related to a mysterious death in the desert. Matt explains why he almost dropped out of intro epi. Oh and if you are wondering why this is the donut episode, Hailey sent Matt donuts after this episode after realizing (60 minutes in….) that she never pressed ‘record’ and Matt’s wife almost sent them back thinking it was a mistake since she had no idea who they were for.

In the episode we mention two papers:

Identifiability, exchangeability, and epidemiological confounding

S Greenland, JM Robins

International journal of epidemiology 15 (3), 413-419

And

Confounding in health research

S Greenland, H Morgenstern

Annual review of public health 22 (1), 189-212

Podcast: Play in new window | Download (Duration: 47:04 — 64.6MB)

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In this episode of Season 2 of SERious Epidemiology, Hailey and Matt go back to chapter 4 of Modern Epidemiology but this time with Dr. Liz Stuart (who may not have trained as an epidemiologist but definitely thinks like an epidemiologist) who has so many insights on what seem like simple concepts. We also get into some of the differences in the way biostatisticians and epidemiologist think about these ideas. And she helps us with some of the disagreements Hailey and I had in the previous episode.

Podcast: Play in new window | Download (Duration: 53:34 — 73.6MB)

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In this episode of Season 2 of SERious Epidemiology, Hailey and Matt dig into chapter 4 of Modern Epidemiology. We focused on the some of the basic building blocks of epidemiology, rates, proportions and prevalence. We found lots to discuss about defining and open and closed populations and the differences (or similarities?) between populations and cohorts. And we debate whether or not this is the “eat your vegetables” chapter. And Matt displays his ignorance of Olympic sports.

Podcast: Play in new window | Download (Duration: 50:19 — 46.1MB)

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In this episode of Season 2 of SERious Epidemiology, Hailey and Matt go back to Chapters 2 and 3 of Modern Epidemiology but this time with guest Dr. Jay Kaufman of McGill University. We focused on the causal inference revolution and how our thinking on some of the issues in the chapter have changed over time as we learn more about these topics.

Podcast: Play in new window | Download (Duration: 45:17 — 62.2MB)

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In this episode of Season 2 of SERious Epidemiology, Hailey and Matt try to finish off Chapter 3 of Modern Epidemiology given they couldn’t get it all into one episode as originally promised. We talked about potential outcomes, sufficient causes models and DAGs (very hard to do in audio only). We focus on the assumptions for causal inference. And we make a pitch for a Modern Epidemiology Audio Book…read by James Earl Jones.