Altair is amazing. I've been using matplotlib going on 12 years now -- Altair didn't feel more productive at first (more typing), but the longer you work with it, the more you realize how the API is a cohesive system. This leads to a more iterative workflow and eventually to better visualizations.
A hazard rate can be thought of as a patient's one day probability of mortality. A hazard ratio is a ratio of two hazard rates, so 1.25 would mean that people in one group were dying at a 1.25x rate relative to a comparison group.
I'd quibble slightly with the interpretation of the Hazard Ratio (or rate) here and say that it's more like "the instantaneous relative risk (or probability, if you like) of the event of interest [in this case mortality but not necessarily] between those with confirmed coronavirus and those without it, given that both groups have survived until time t and holding [set of covariates of interest, e.g. race, age, etc] constant."
Don't mean to sound like a pedant here even though it totally reads that way. I figured with enough epidemiology in the news these days, people may see a lot of "Hazard ratios" or "Cox regression" or "survival analysis" and be at risk of some confusion. I work with these concepts and I get tripped up myself sometimes.
+1 to this, I've had multiple teams that have shied away from airflow because of its operability & deployment story, despite needing something that has all its features.
In terms of radically different takes on workflow engines, I'm very interested in reflow. I haven't used it enough to know if the rough edges are a deal breaker.
I think it's pretty easy for this discussion to become about personalities and not methodologies. Gelman is pretty brash and speaks his mind, so he could be correct on methodology and have that get lost in the noise.
So I'll give my opinion as a datascientist: null hypothesis significance testing is broken, the whole thing needs to get chucked. It's not fixable, and it's not only p-hacking that's the problem. Go read Frank Harrell, I like his writings on the topic more than Gelman's.
Thank you for the comment and the link. I agree with most of the points listed there. And GAM is a great tool when there is non-linear and non-monotonic relation between the response and independent variables. GAM has good interpretability but it is still somehow difficult to understand in some business environment. For example, in credit scoring, logistic regression with binning is still widely applied.
In my experience, most the time people use binning, it's straightforward to demonstrate that their binning+model is equivalent to restricted forms of more general models (e.g. common general additive / structural equation model). Sometimes binning is useful, because it makes them much easier to estimate.
However, people's rationales for why they should bin is often that it makes the model better / more interpretable, without actually testing the more restricted binned model against the more general one. There's certainly something to be said for knowing your audience when choosing a model, though :).
Classic post! This post is like a gentle gateway to the world of Bayesian statistics -- check out Cameron Davidson Pilon's free book if you want to go deeper.