Causal Inference: Applying Bradford Hill Criteria to Regression Models – Recorded Livestream
Channel: Monika Wahi
Date: 2022-04-16

*Note: I may be compensated, but you will not be charged, if you click on the links below.

This livestream about inferential statistics was originally broadcast on April 16, 2022. Monika Wahi covered the following topics:
* How to connect the a priori hypothesis with the regression model you are building
* How to think about the slopes in your final fitted model in terms of their relative impact and importance on the question of causal inference
* What the Bradford Hill Criteria of Causation actually are, and how Monika likes to apply them
* Applying Bradford Hill criteria to two models to estimate the evidence of causation

Download the slides Monika presents here: https://buff.ly/3vohQRV 

I listed the links I mention below as they come up in the livestream. Enjoy!
01:30 CJ Hutchings, Boston-based musician, painter, and writer, contributes background music to Monika’s videos. Follow him on Spotify: https://buff.ly/37aeoCl
03:21 Video of ggplot2 code with CJ’s music in the background: https://youtu.be/vsTDkz2nESQ
06:16 Monika starts slide presentation
06:35 Review: What are we actually doing in “causal inference”? Epidemiology terminology: Exposure, Outcome (e.g., disease), and Confounders.
08:44 Explanation of confounders
10:15 Posing a hypothesis, then considering “low income” as a potential confounder in the relationship between the exposure “lack of exercise” and “heart attack” in the hypothesis
11:10 Example of apply the “not on the causal pathway” criterion for confounders
12:10 Tips on assessing the “not on the causal pathway” criterion
13:30 Measure all your confounders!
13:55 Good exercise to do a priori: State exposure, state outcome, and list confounders
14:03 How do you figure out what confounders to measure? Monika’s tips
15:00 Tips for stating a hypothesis a priori, and why you should do it
16:30 What to write down a priori about the hypothesis
16:54 Problem with administration data: lack of needed variables
17:43 Courses “Designing Big Data Healthcare Studies” Parts 1 and 2: https://linkedin-learning.pxf.io/NKN0JO
18:22 Discussion of regression using a binary outcome specifically in health data: logistic vs. survival analysis
20:04 If your model is goofy 1) it’s incorrect, or 2) it’s correct, and now you need to figure out why that is
20:40 Overview of regression model specification in causal inference
21:36 Bursac/Hosmer article on stepwise selection: https://scfbm.biomedcentral.com/articles/10.1186/1751-0473-3-17 
22:28 Interpreting the exposure slope after executing the modeling process
24:08 How to deal with it if your exposure slope is not statistically significant: diagnose collinearity
25:17 How to deal with it if your exposure slope is statistically significant
25:25 Do you present/interpret the slopes for the confounders?
26:44 Discussion of “noise” data in public health vs. engineering
27:50 Why healthcare cannot have the same kind of protocols as engineering
28:20 History/development of Bradford Hill Criteria
29:30 Link to web page about Bradford Hill Criteria: https://www.statsdirect.com/help/basics/causality.htm
31:44 Walking through Monika’s Bradford Hill Criteria application process
33:05 Temporality
34:26 Dose-response gradient
36:32 Strength of association
36:58 Discussion of case-control study designs
38:23 Consistency
39:20 Coherence
41:45 Case study 1: Cross-sectional study: “Association of marijuana use with oral HPV infection and periodontitis among Hispanic adults: Implications for oral cancer prevention”, available here: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6647026/ 
42:40 Challenge with measuring stigmatized behaviors
44:15 Do not interpret crude/unadjusted estimated!
45:30 Relationship between smoking tobacco, smoking marijuana and periodontitis
47:05 Interpreting and assessing causality for 2.90 odds ratio for marijuana smoking and severe periodontitis
51:13 Three kinds of studies where you can do causal inference: cross-sectional, case-control, and cohort
54:12 Case study 2: Cohort study: “Chronic kidney disease and use of dental services in a United States public healthcare system: a retrospective cohort study,” available here: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3368751/ 
58:30 Process of doing cohort studies with existing data: How to build the analytic file