Can we talk about how SLOW the FDA is — my results so far

NurseAsh_DET ↑ said:

My own curve sits about four points below the published mean and I spent two months assuming that meant something was wrong with me or with my…

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.

BethLabQueen ↑ 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 25,000 GLP-1 users vs matched controls showed reduced all-cause mortality (HR 0.81) over 5 years of follow-up[1].

These results complement the RCT data and suggest the benefits translate to real-world populations.

A narrower follow-up, since the general answer is now clear:

How would you tell the difference between that and the alternative explanation?

Reporting back.

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.