How can a network meta-analysis compare untested pairs?

By linking them through shared comparators — if both were tested against placebo, the difference can be estimated through that common arm.

What is transitivity?

The assumption that the trials being linked are similar enough for the comparison to mean something. Differing populations or durations undermine it.

Is it as good as a head-to-head?

No — it sits below a direct randomised comparison, though above the informal side-by-side reading people do without it.

GLP-1 & Incretin Science

Comparing Things That Were Never Compared

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

A network meta-analysis compares treatments that were never tested against each other by linking them through comparators they share — usually placebo. It produces indirect estimates, and rests on the assumption that the trials being linked are similar enough to connect.

Key facts

Purpose: Compare without a head-to-head
Method: Link trials through shared comparators
Key assumption: Transitivity — trials are comparable
Threatened by: Differing populations, durations, endpoints
Weaker than: A direct randomised comparison
Stronger than: Informal cross-trial comparison

The problem it solves

Most treatments are never tested directly against each other, because head-to-head trials are expensive and commercially risky. Yet the question of which works better is exactly what prescribers and payers need answered. Network meta-analysis extracts an answer from trials that already exist rather than requiring a new one.

How the linking works

If A was tested against placebo and B was tested against placebo, the difference between A and B can be estimated through their shared comparator. Extend that across many treatments and comparators and you get a network — hence the name — in which most pairs are connected by some path even where no direct edge exists.

Research material referenced

Retatrutide 10mg — third-party HPLC tested

View — £59.99

The assumption everything rests on

Transitivity: that the trials being linked are similar enough that the comparison means something. If the trials of A enrolled healthier participants, ran shorter, or measured a different endpoint than the trials of B, the indirect comparison inherits those differences and attributes them to the treatments.

Why it is nonetheless better than what people do informally

Reading two trial results side by side and concluding one drug beats another is an indirect comparison too — just an undisciplined one, with no adjustment and no acknowledgement of uncertainty. A network meta-analysis does the same thing explicitly, propagates uncertainty into the estimate, and can be examined for whether the assumption holds.

Where it sits in the hierarchy

Below a direct randomised head-to-head, which is why REDEFINE 4 comparing CagriSema against tirzepatide, and TRIUMPH-5 comparing retatrutide against tirzepatide, carry weight that no network analysis can substitute for. Above informal comparison. It is a legitimate method used where the better evidence does not exist.

How to read one critically

Look at what the network actually connects and how. A comparison resting on one small trial as its only bridge is fragile regardless of how much data sits elsewhere in the network. Look at whether populations and durations across the linked trials are genuinely comparable, and whether the authors examined that rather than asserting it.

Quick reference

	Head-to-head	Network meta-analysis	Informal comparison
Randomised between arms	Yes	Within trials only	No
Uncertainty propagated	Yes	Yes	No
Assumption required	Minimal	Transitivity	Unstated

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

How can a network meta-analysis compare untested pairs?: By linking them through shared comparators — if both were tested against placebo, the difference can be estimated through that common arm.
What is transitivity?: The assumption that the trials being linked are similar enough for the comparison to mean something. Differing populations or durations undermine it.
Is it as good as a head-to-head?: No — it sits below a direct randomised comparison, though above the informal side-by-side reading people do without it.

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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