Why is effectiveness lower than efficacy?

Selection, trial monitoring and real-world persistence. The first two make trial populations perform better; the third removes benefit entirely for those who stop.

Does the gap mean the trial was flawed?

No. A trial establishes whether a drug can work, which requires control that necessarily limits generalisability. They are two questions asked in order.

How is effectiveness measured?

Observational data analysed with trial-like discipline — target trial emulation being the method built for exactly this.

GLP-1 & Incretin Science

Why a Drug Works Less Well Than Its Trial Said

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

Efficacy is what a treatment achieves under trial conditions with selected participants and monitored adherence. Effectiveness is what it achieves in routine care. The second is reliably smaller, and the reasons are structural rather than mysterious.

Key facts

Efficacy: Trial conditions, selected participants
Effectiveness: Routine care, everyone
Direction: Effectiveness is reliably lower
Main causes: Selection, monitoring, persistence
Formalised by: Efficacy vs treatment-policy estimand
Measured by: Cohort studies, target trial emulation

Selection is the first cause

Trials exclude people. Those with competing conditions, on interfering medications, unlikely to attend visits, or unable to consent are screened out. The resulting population is healthier and more able to comply than the population who will eventually receive the drug, and the trial result belongs to the group that was studied.

Monitoring is the second

Trial participants are seen regularly, reminded, and have adverse effects managed promptly by people whose job is to keep them in the study. That support is itself an intervention. Removing it does not merely remove convenience — it removes a mechanism that was helping produce the result.

Research material referenced

Retatrutide 10mg — third-party HPLC tested

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Persistence is the third and largest

The most efficacious treatment achieves nothing in someone who has stopped taking it. Where real-world persistence is substantially lower than trial completion, the population-level effect falls proportionately, and for a chronic condition where stopping reverses the benefit, this dominates the other causes.

How the gap is measured

Observational data analysed carefully, which is where target trial emulation earns its place. It applies trial-like design discipline to routine records, producing an estimate of what happens in practice while removing the analytical biases that would otherwise make such comparisons uninterpretable.

Why the gap is not evidence of a bad trial

A randomised trial is designed to establish whether a drug can work, under conditions favourable enough to detect an effect if one exists. That is a necessary first question, and answering it well requires exactly the control that limits generalisability. Efficacy and effectiveness are two questions, and it is reasonable to answer them in that order.

How to read any headline figure

Ask which question it answers. A 20% reduction from a randomised trial describes selected participants under monitoring, on the efficacy estimand, among those who continued. Every one of those qualifiers moves the number, and a figure travelling without them is describing a situation almost nobody is in.

Quick reference

	Efficacy	Effectiveness
Population	Selected	Everyone treated
Support	Trial monitoring	Routine care
Adherence	High, encouraged	Variable
Typical source	Randomised trial	Cohort, emulation

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

Why is effectiveness lower than efficacy?: Selection, trial monitoring and real-world persistence. The first two make trial populations perform better; the third removes benefit entirely for those who stop.
Does the gap mean the trial was flawed?: No. A trial establishes whether a drug can work, which requires control that necessarily limits generalisability. They are two questions asked in order.
How is effectiveness measured?: Observational data analysed with trial-like discipline — target trial emulation being the method built for exactly this.

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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