greg_boulder said:The gap between trial results and real-world results is consistent and it is not fraud.
Forest plot interpretation for the the trial evidence meta-analysis: when reading the pooled estimate, pay attention to:
- Point estimate (HR/RR/OR) — center of the diamond
- Confidence interval width — precision of the estimate
- I² statistic — heterogeneity across studies
- Individual study weights — are results driven by one large trial?
- Prediction interval — range of plausible true effects in future settings
The the trial evidence meta-analysis shows a pooled RR of 0.85 (95% CI 0.67-0.90), I²=58%. This is a robust and consistent effect.
A narrower follow-up, since the general answer is now clear:
How would you tell the difference between that and the alternative explanation?
SleepDoc_PDX said:Forest plot interpretation for the the trial evidence meta-analysis: when reading the pooled estimate, pay attention to: Point estimate (HR/RR/OR) —…
SleepDoc_PDX said:...regarding the trial evidence...
I think this is an underappreciated point. To expand on it with some data:
A recent meta-analysis of 15 RCTs (n=12,300) found that the trial evidence was associated with a clinically meaningful effect size across diverse patient populations[1].
The NNT was 12, which is comparable to statins for secondary prevention. That's a strong clinical argument for this approach.
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Follow-up: I read the paper rather than the summary and the qualifier I was missing was in the second paragraph of the results.
sarah_TO said:SleepDoc_PDX 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.