Starts with the story of Rofecoxib (Vioxx).
Presents the evidence that didn’t get into the NEJM because one of the study authors was a paid consultant on the drug company producing Vioxx.
Carl recalls that he prescribed Vioxx and is vexed by the probability of harm he may have caused. Due to a COI impacting on the evidence he used to inform his medical practice.
And this was preventable if a better system was in place.
This slide is always revealing.
“Conflicts don’t impact on me. But they do on my peers”.
And they are starting this website (will share link if I find it). Just a small matter of waiting for @GOVUK and @gmcuk to make this standard practice. #whatareyouwaitingfor
Lots of takes on this. Many are what people have anyway regardless of this paper, such is the general diet/nutrition discourse, and particularly around UPF.
For those of you interested in what an actual evidence-based approach to this paper looks like, buckle up🧵
In my view it is a good example of pervasive issue in the way most medical/health research is interpreted, and this even includes the authors themselves.
What’s the issue?
IGNORING UNCERTAINTY!
2/
A key function of a systematic review is to inform as to how certain we can be that the available data provides an observable truth.
There are different methods a review team can take. One of the most commonly used in med/health research is GRADE.
"It's going to protect YOU..."
"It will reduce YOUR risk of a heart attack, cancer, diabetes etc".
We see this all the time when it comes to medical treatments and health interventions.
I'm going to show why 99.9% of the time this type of phrasing/framing ("YOU/R") is wrong🧵
When people hear these phrases with "YOU" / "YOUR" in them, it's most likely they will perceive the benefits & risks of treatment/intervention in relation to their own personal benefit/risk. The thing is, we do not, & cannot, know your own personal benefit/risk.
The vast majority of the time we only have information on groups of people (samples). Benefits/risks in this context relate to the numbers of people (with a similar health profile) among the group who either do or do not experience a given health outcome.
out of frustration of seeing an organisation promote evidence from a commentary piece as if it were proof a question had been answered. No uncertainty. Proof.
/1
The same organisation didn't mention a systematic review attempting to answer the same question (which happened to show uncertainty). Yes, the organisation was aware of the review's existence.
This signals, to me, an agenda. It's also a good example of the state of things.
/2
The state of things in relation to evidence and "evidence-based". It's a state I worry about, hence the original tweet.
This isn't about the topic/question. It's about the principle. Of promoting selected information as if it is proven fact, ignoring info that might contest.
/3
Our latest publication revisits a well-known problem: reporting of relative effect estimates without absolute effects in journal publications of clinical trials:
First an intro to the problem. A practical example is probably best here. Take a look at the image. How many more people are at increased risk bowel cancer?
Lots of folk already commented on “1 or 2 dose”. One thing that comes up often is “efficacy”, followed by numbers like “80%”, 95%”. In many cases, it reads as if folk think this is how much YOUR chance of getting the virus is reduced by. That’s incorrect.
These figures are actually the relative risk reduction (RRR) of infection with the vaccine. Eg. 2000 people without Covid-19 - 1000 vaccinated (group 1), 1000 not vaccinated (group 2).
200 people (20%) in group 2 get Covid-19
10 people (1%) in group 1 get Covid-19
= 95% RRR
The absolute effect is 19% (difference between 1% and 19%).
Another way of putting it = in 1000 people who don’t have Covid and are not vaccinated, 200 will catch it.
If the same 1000 people had the vaccine, 10 would get it, meaning 190 will be spared.