mike_nyc said:Relative and absolute effects need reading together.
mike_nyc said:...regarding the trial evidence...
I think this is an underappreciated point. To expand on it with some data:
A recent meta-analysis of 12 RCTs (n=8,400) found that the trial evidence was associated with a significant effect size across diverse patient populations[1].
The NNT was 8, which is comparable to antihypertensives for stroke reduction. That's a strong clinical argument for this approach.
One thing that is still open after TrialNerd_Beth’s answer:
How would you tell the difference between that and the alternative explanation?
josh_phd_bmore said:mike_nyc said: ...regarding the trial evidence...
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 0.79 (95% CI 0.72-0.89), I²=37%. This is a robust and consistent effect.
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Update — my curve sits below the published mean and the explanation is that the trial arm had support I do not have. That was reassuring rather than otherwise.
sophie_paris said:Forest plot interpretation for the the trial evidence meta-analysis: when reading the pooled estimate, pay attention to: Point estimate (HR/RR/OR) —…
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.