Takeaways from 'On the uses and abuses of regression models'
This weekend I found an interesting new preprint by Carlin and Moreno-Betancur on arxiv titled “On the Uses and Abuses of Regression Models” so I had to check it out. The article focuses on medical literature, where regressions – even in my experience – often seem done almost automatically and then interpreted depending on the the desired question as opposed to with respect to model construction. “Garbage can” regressions to find “important risk factors” abound, as do repeat fittings of simple models in an attempt to describe a joint distribution. One of my favorite examples to show in class of the issues with the latter is a 2008 paper by Wang et al. that to date has been cited more than 1,000 times. The topic of the paper is an analysis of NHANES data with the aim of predicting the prevalence of obesity in the US. They desire to describe how different subgroups of Americans, that is the different genders and ethnicities, fare. Instead of fitting a joint model, they fit multiple linear models. This leads to fun results in their Table 2, where all Americans of all races and ethnicities will be obese by 2048, yet all men won’t be obese until 2051. Mexican-American men fare the best, as they escape being part of all Americans somehow and won’t reach 100% prevalence until 2126. ...
