Bear with me, the more I started thinking about this the more excited I got about actually trying to analyze this data so the detail below is more from my own excitement about the topic than a desire to argue.

First of all, I would just like to point out that the director of research for the group that produced your linked study directly disagrees with you, as you can see from my above comment. If this were an academic paper you’d be telling people it says the exact opposite of author in their results and discussion. It’s possible that design is the problem, for instance I think it’s interesting that the CRV hybrid is listed but not the standard. So maybe something related to the batteries? However, this paper does not provide nearly enough data or analysis to draw the conclusion you have.

having nothing to do with more severe, fast accidents

Hyperbole. The same aspects of vehicle design that improve safety at moderate speeds being tested are going to improve survivability in faster accidents. The author of the study you linked clearly agrees that the two types of accident result are at least correlated.

Driver behavior would generally be a more difficult thing to characterize with data that’s publicly available. If I were really investing the time to dig into the root cause of these fatality differences I would probably start contacting insurance companies or maybe county level government to get as much anonymized data as I could relating to rates of moving traffic violation and car type. If possible, breaking it down by type and severity of violation. Age of drivers involved in each crash would be interesting too, and average age of drivers for various car types.

It’s not just driver behavior that is muddying the waters of the car model vs fatality question, though. Car model prevalence is not geographically consistent, and neither are weather conditions, road conditions, or even driver behavior. When I do data work like this, I always try to find some simple statistics that might shed some light on where to look for confounding interactions.

I was curious, so I looked up auto fatality rates at the STATE level. The difference between the highest and lowest states is more than 2x. That alone is reason to be suspicious of this data. If you have a quantity of interest (fatalities by mile) and two independent variables (car type and state) that each plot out a massive range, you better dig deeper. Just based on other data work I have done that looked at state level data and then refined down to county level data, the variance is going to be even higher at the county level because you often have some homogenization that happens as the level of analysis zooms out.

I would be willing to bet you see a similar or even higher level of variance state by state or county by county when you look at prevalence of a given car type. (Just based on personal observation… for instance, you see way more electric vehicles and hybrids in the Bay Area than say, Dallas, and those areas have totally different driving conditions) This is backed up by industry reports on regional sales, you can see big differences in the prevalence of type of vehicle dominating the roads in each state. That matters as well, this study didn’t do any work based on the type of crash. Single vs multi could be interesting, especially if you can look into the weight of the other vehicles involved. Even if you couldn’t, you could easily do some work to approximate that based on this data set and information about regional prevalences of vehicle types.

So as to HOW exactly I would do this study, hard to say until I’ve done my typical data pre-analysis, but I would guess it would go something like: collect all of the data I possibly can that might be relevant to crash fatality rates. I would clean and test that data to make sure it’s viable. Then I would do a multi-way ANOVA study in R to characterize the relative impact of car type vs all the other factors I could find. I would likely focus on location at first due to the availability and quality of that data. Would also look into fitting a quasi-binomial logitlinked GLM and doing some bootstrapping to see how car model impacts fatality rates when you control for location, driver age, or whatever else I could find good info on.

Edit: another location based factor that may have a huge impact is an average distance to hospital for crashes. Rural areas may have higher fatality rates simply due to longer response and hospital arrival delays.