Short answer first, then the reasoning. The gap between trial results and real-world results is consistent and it is not fraud. Trial participants get titration by protocol, scheduled contact, free drug and dietetic support; removing that infrastructure costs a few percentage points every time it has been measured. When your own curve sits below the published mean, that is the likeliest explanation before anything about you or your material.

sarah.morrison 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:

1. Point estimate (HR/RR/OR) — center of the diamond
2. Confidence interval width — precision of the estimate
3. I² statistic — heterogeneity across studies
4. Individual study weights — are results driven by one large trial?
5. Prediction interval — range of plausible true effects in future settings

The the trial evidence meta-analysis shows a pooled RR of 0.80 (95% CI 0.71-0.90), I²=38%. This is a robust and consistent effect.

mia_MS2 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…

Same position here, arrived at the long way round. Read four things before the headline number. The population, because trial populations are selected and supported in ways that real cohorts are not. The comparator, because "better than placebo" and "better than the current standard" are different claims and get reported identically. The primary endpoint as pre-registered, because a secondary endpoint promoted after the fact is a hypothesis rather than a finding. And the completion rate, because a large effect in the half of participants who finished is a different result from a large effect in everybody enrolled.

Clinical perspective, offered as context rather than as advice.

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 MI incidence (HR 0.78) over 5 years of follow-up[1].

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