Skip to main content

Some wrong lessons people will learn from the president’s illness, hospitalization, and expected recovery

Jonathan Falk writes about the president’s illness:

I [Falk] would think it provides a focused opportunity to make a few salient statistical education points.

First, a 6 percent mortality rate (among old people with comorbidities) is really bad, but any single selected person is really quite unlikely to die, or even be really sick. Same with all the reports about blood clots, six month recovery times, etc., etc. Even more unlikely. A prediction: when Trump feels fine in a couple of days this will be taken as one more piece of evidence that this is not a serious disease, which is statistically illiterate on a number of levels.

Second, the reference group (old people with comorbidities) implicitly assumes a standard level of care. (Changes in the standard of care is one of the main reasons the death rate has fallen so much from the average.) Trump’s probabilities are way better than that because he gets care that very few other people in the world get.

Third, it will be interesting to see retrospective assessments of the impact of whatever treatments he got. And of course how poor such retrospective inferences are.

That first point reminds me of the hookah story.



from Statistical Modeling, Causal Inference, and Social Science https://ift.tt/36AZNgR
via IFTTT
Labels:

Comments

Post a Comment

Popular posts from this blog

Controlling legend appearance in ggplot2 with override.aes

[This article was first published on Very statisticious on Very statisticious , and kindly contributed to R-bloggers ]. (You can report issue about the content on this page here ) Want to share your content on R-bloggers? click here if you have a blog, or here if you don't. In ggplot2 , aesthetics and their scale_*() functions change both the plot appearance and the plot legend appearance simultaneously. The override.aes argument in guide_legend() allows the user to change only the legend appearance without affecting the rest of the plot. This is useful for making the legend more readable or for creating certain types of combined legends. In this post I’ll first introduce override.aes with a basic example and then go through three additional plotting scenarios to how other instances where override.aes comes in handy. Table of Contents R packages Introducing override.aes Adding a guides() layer Using the guide argument in scale_*() Changing multiple aesthetic par...
Post a Comment
Read more

Using RStudio and LaTeX

(This article was first published on r – Experimental Behaviour , and kindly contributed to R-bloggers) This post will explain how to integrate RStudio and LaTeX, especially the inclusion of well-formatted tables and nice-looking graphs and figures produced in RStudio and imported to LaTeX. To follow along you will need RStudio, MS Excel and LaTeX. Using tikzdevice to insert R Graphs into LaTeX I am a very visual thinker. If I want to understand a concept I usually and subconsciously try to visualise it. Therefore, more my PhD I tried to transport a lot of empirical insights by means of  visualization . These range from histograms, or violin plots to show distributions, over bargraphs including error bars to compare means, to interaction- or conditional effects of regression models. For quite a while it was very tedious to include such graphs in LaTeX documents. I tried several ways, like saving them as pdf and then including them in LaTeX as pdf, or any other file ...
Post a Comment
Read more