PharmD_Rodriguez said:The gap between trial results and real-world results is consistent and it is not fraud.
PharmD_Rodriguez said:...regarding the trial evidence...
I think this is an underappreciated point. To expand on it with some data:
A recent meta-analysis of 18 RCTs (n=15,600) found that the trial evidence was associated with a robust effect size across diverse patient populations[1].
The NNT was 15, which is comparable to antihypertensives for stroke reduction. That's a strong clinical argument for this approach.
One thing that is still open after pat_auckland’s answer:
What would you measure differently if you were starting again?
dave_SLC said:PharmD_Rodriguez said: ...regarding the trial evidence...
Bayesian meta-analysis perspective on the trial evidence: traditional frequentist meta-analyses report point estimates and confidence intervals. Bayesian approaches provide probability distributions that are more intuitive for clinical decision-making.
For example: "There is a 98.5% probability that semaglutide 2.4mg produces >10% weight loss vs placebo" is more actionable than "RR 3.4, 95% CI 2.8-4.1, p<0.001."
The the trial evidence evidence is strong under both frameworks, but Bayesian analysis better communicates the degree of certainty for individual patient counseling.
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View ResultsOP back with an update, since a thread like this is useless without one.
Follow-up: I read the paper rather than the summary and the qualifier I was missing was in the second paragraph of the results.
LipidDoc_ATL said:Bayesian meta-analysis perspective on the trial evidence: traditional frequentist meta-analyses report point estimates and confidence intervals.
Propensity score matching studies and the trial evidence: when RCTs aren't available for a specific question, propensity score-matched observational studies can provide useful evidence.
A recent PSM study of 18,000 GLP-1 users vs matched controls showed reduced MI incidence (HR 0.78) over 4 years of follow-up[1].
These results complement the RCT data and suggest the benefits translate to real-world populations.
[1] Registry-based cohort study, pre-print 2024.