Simon Gates writes:
Where is an issue that has had a lot of publicity and Twittering in the clinical trials world recently. Many people are promoting the use of the “fragility index” (paper attached) to help interpretation of “significant” results from clinical trials. The idea is that it gives a measure of how robust the results are – how many patients would have to have had a different outcome to render the result “non-significant”.
Lots of well-known people seem to be recommending this at the moment; there’s a website too (http://fragilityindex.com/ , which calculates p-values to 15 decimal places!). I’m less enthusiastic. It’s good that problems of “statistical significance” are being more widely appreciated, but the fragility index is still all about “significance”, and we really need to be getting away from p-values and “significance” entirely, not trying to find better ways to use them (or shore them up).
Though you might be interested/have some thoughts as it’s relevant to many of the issues frequently discussed on your blog.
My response: I agree, it seems like a clever idea but built on a foundation of sand.