sarah.morrison said:Read four things before the headline number.
Pushing back on sarah.morrison here. I would add the less popular caveat: these trial populations under-represented several groups, older adults and the highest BMI categories among them. The results probably generalise, and "probably" should be stated as an assumption rather than dropped.
AmyNC_wife said:I keep finding that the number in the press summary and the number in the paper are not the same number, and the difference is always in the same…
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.
labquiet_amy said:I would add the less popular caveat: these trial populations under-represented several groups, older adults and the highest BMI categories among them.
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 heart failure hospitalization (HR 0.74) 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.
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View ResultsFollowing on from JessicaM_2024 — and this may be the naive question:
What would you measure differently if you were starting again?
Reporting back.
Follow-up: I read the paper rather than the summary and the qualifier I was missing was in the second paragraph of the results.