Does a ROR of 19 mean a nineteen-fold risk?

No. It means the event was disproportionately represented among reports for that drug. Without a denominator, no risk can be calculated from it.

Are these databases useless then?

Far from it. They detect rare events trials are too small to find. The error is treating a detection tool as a measurement tool.

What is notoriety bias?

Publicity about a possible drug-event association increases reporting of that association, which can inflate disproportionality measures independently of any real change in risk.

GLP-1 & Incretin Science

How to Read a Pharmacovigilance Signal

JMWritten & reviewed by Jack Muncaster · Founder, UK PeptidesLast reviewed 2026-08-233 cited sources

Disproportionality measures such as PRR and ROR describe how often an adverse event appears in a spontaneous reporting database relative to other events — not how often it occurs in people. They detect signals for investigation. They cannot estimate risk, because the database has no denominator.

Key facts

PRR: Proportional reporting ratio
ROR: Reporting odds ratio
Source: Spontaneous reporting databases (FAERS, Yellow Card)
What they measure: Disproportionate reporting
What they cannot measure: Incidence or risk
Missing element: A denominator — how many people took the drug
Correct use: Signal detection, prompting further study

What a spontaneous reporting database is

FAERS in the United States and the Yellow Card scheme in the UK collect reports of suspected adverse reactions submitted voluntarily by clinicians, patients and manufacturers. They are enormously valuable for detecting rare events that trials are too small to find. They are also, by construction, a collection of reports rather than a study — nobody was enrolled, nothing was randomised, and no one counted how many people took the drug without incident.

What PRR and ROR actually compute

Both compare how frequently a particular event is reported for a particular drug against how frequently it is reported for everything else in the database. A high value means this event is disproportionately represented among reports for this drug. That is a statement about the composition of a report collection. It is not a statement about how many people experienced the event.

The missing denominator

To calculate a risk you need to know how many people were exposed. A spontaneous database does not know this. If a drug is taken by ten million people and generates two hundred reports of an event, and another is taken by ten thousand and generates twenty, the crude report counts are wildly misleading and the disproportionality measures do not fix that — they were never designed to.

Reporting is not random

Several biases push in the same direction. Newly launched drugs attract more reporting than established ones. Media attention to a possible association increases reporting of that association, sometimes dramatically — a phenomenon known as notoriety bias, which can manufacture a signal from publicity alone. Serious events are reported more than mild ones. None of these are flaws in the system so much as properties of it, and all of them affect disproportionality values.

A worked example

A disproportionality analysis reporting a PRR of 18.9 and ROR of 19.4 for liraglutide and acute pancreatitis is frequently repeated as though it meant a nineteen-fold risk. It does not. It means reports of pancreatitis were disproportionately represented among liraglutide reports relative to the rest of the database. Whether that reflects a real elevated risk, and of what magnitude, requires a cohort study with a defined population — which is exactly what such a signal should prompt.

What signals are for

They are the first stage of a working pharmacovigilance system, not its conclusion. A signal triggers investigation; a cohort or case-control study with a real denominator estimates risk; a regulator weighs that against benefit and acts. The MHRA's February 2026 semaglutide update is the visible end of exactly that pipeline — and note that it came with a frequency estimate, up to 1 in 10,000, which is the sort of figure disproportionality analysis can never produce.

Quick reference

	Disproportionality (PRR/ROR)	Cohort study
Data source	Spontaneous reports	Defined population
Denominator	None	Known
Can estimate incidence	No	Yes
Can estimate risk	No	Yes
Vulnerable to reporting bias	Yes, strongly	Much less so
Correct role	Detect signals	Quantify them

Extended research context

The GLP-1 & Incretin Science deep dive

Deep dive: the two routes to a bigger effect

Every compound trying to beat GLP-1 alone has taken one of two routes. The first adds more receptors from the same hormone family — GIP in tirzepatide, GIP and glucagon in retatrutide. The second adds a non-incretin satiety hormone, which in practice means amylin: CagriSema combines cagrilintide with semaglutide, and amycretin engages both receptors from one molecule. Both routes work, because they recruit signalling pathways that do not fully overlap. Neither has escaped the constraint that binds all of them, which is that gastrointestinal tolerability worsens as effect size grows.

Deep dive: why a percentage is not a result

The most-quoted numbers in this field are the least comparable. REDEFINE 1 reported 22.7% and 20.4% for the same compound in the same trial — the first among participants who adhered to treatment, the second across everyone randomised. TRIUMPH-1 reported 28.3% in an uncomplicated obesity population while TRIUMPH-3 reported up to 22.6% in adults with established cardiovascular disease, using the same compound. Before any two figures can be compared they have to match on estimand, population, duration, comparator and whether the number is placebo-adjusted. Most published comparisons match on none of them.

Deep dive: what happens after the trial stops

Every headline figure describes weight while treatment continues. The STEP-1 extension found that a year after semaglutide was stopped, participants had given back roughly two-thirds of what they lost, moving from 17.3% mean reduction to a net 5.6% — though average weight remained below baseline and nearly half stayed at least 5% down. Meta-analysis puts regain at around 0.8 kg per month. This is why maintenance studies such as TRIUMPH-6 matter more to the field's future than another two points of peak reduction.

Research applications

▸Comparing incretin and amylin compounds on a like-for-like basis
▸Interpreting estimands, thresholds and placebo-adjusted figures in trial reports
▸Tracking the obesity pipeline across sponsors and jurisdictions
▸Understanding receptor pharmacology behind GLP-1, GIP, glucagon and amylin
▸Distinguishing licensed medicines from investigational compounds

Handling checklist

✓Identify which estimand a quoted percentage comes from before citing it
✓Check the trial population and baseline BMI against the comparison you are making
✓Confirm the duration and whether the reduction curve had plateaued
✓Read discontinuation rates alongside efficacy figures
✓Verify every NCT identifier against ClinicalTrials.gov rather than secondary reporting

Common research-handling mistakes

Learnt from thousands of researcher orders across our UK labs.

✗ Comparing headline percentages across different trials

Fix: Population, duration, estimand and comparator all differ; the numbers are not interchangeable.

✗ Quoting the larger of two figures from the same trial

Fix: Name the estimand. Efficacy and treatment-policy answer different questions.

✗ Treating peak reduction as a durable outcome

Fix: Substantial regain follows cessation across the class; peak figures describe a maintained state.

✗ Assuming an oral route means a weaker mechanism

Fix: Route and receptor count are independent. Orforglipron is weaker because it hits one receptor, not because it is a tablet.

✗ Reading investigational compounds as available treatments

Fix: Most of this pipeline holds no authorisation anywhere; mazdutide is approved only in China.

Continue researching

Peer-reviewed guides, comparators and matched reference materials.

Frequently asked questions

Does a ROR of 19 mean a nineteen-fold risk?: No. It means the event was disproportionately represented among reports for that drug. Without a denominator, no risk can be calculated from it.
Are these databases useless then?: Far from it. They detect rare events trials are too small to find. The error is treating a detection tool as a measurement tool.
What is notoriety bias?: Publicity about a possible drug-event association increases reporting of that association, which can inflate disproportionality measures independently of any real change in risk.

Primary sources & clinical trials

Peer-reviewed research and registered trials from PubMed, ClinicalTrials.gov, PubChem, FDA and NIH. All links open in a new tab and point to the primary source, so every claim can be verified at origin.

Written and reviewed by

Jack Muncaster · Founder, UK Peptides

Jack founded UK Peptides in Manchester after repeatedly receiving research compounds with missing or recycled paperwork. He is responsible for supplier selection, batch release decisions and the content published in this research library. Every article here is sourced to primary literature and every product page to a signed third-party certificate.

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