1/

Interesting study coming out suggesting full lockdowns offer no benefit over softer measures.

Great they released the code! It is worth a deeper dig as the journal is fairly vague about the data used. I've stuck the source code on my GitHub here: github.com/dr-harper/NPI-…
2/

It might sound fair to accuse me of bias, with the only reason that I question this study because it challenges my view of lockdowns.

I have seen papers suggest lockdowns don't have a big impact, but this seems to suggest they have a negative impact? What is going on Image
3/

Now, one threat not considered by the paper is potential confounding variables. See statisticshowto.com/experimental-d…

Is it fair to compare international countries purely on case rates and lockdown measures? Are there broader differences? Highly possible Image
4/
So lets look at the code!

It is worth noting that for peer-reviewed journals, reviewing the code is normally optional!

This job is made harder when the code is confusing. So instantly my alarm bells are off when I am seeing a mix of R and Stata. Image
5/

The journal provides fairly minimal information about the data. Specifically, no timespan is provided. Looking at this we have fairly different temporal temporal coverage between February - September. Unclear how much was used for the modelling. Image
6/

I'd love to see if others have any opinions on this! Unfortunately, my knowledge of #STATA is non-existant, so I can't be too detailed on the modelling side of things
🙁 feel free to fork the code and play around! @VictimOfMaths
7/

Don't get me wrong. Peer-reviewed journals are definitely a gold standard when it comes to evidence, so I wouldn't want suggest otherwise. But given the potential for interpretation in this case, it is crucial to be critical.
8/

Just adding some other analysis which is 100 times better than mine!

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