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Reading the Evidence

Why a "20% Reduction" Headline Usually Means Less Than It Sounds

A 20 percent relative reduction can be worth one event prevented in thirteen people or one in a thousand. The difference is the baseline risk, and headlines almost never print it.

What a 20 percent risk reduction actually tells you

It tells you the ratio between two event rates, and nothing else. An untreated rate of 8 percent against a treated rate of 6.4 percent is a 20 percent relative reduction. An untreated rate of 0.5 percent against a treated rate of 0.4 percent is also a 20 percent relative reduction. The two situations are not remotely comparable for someone deciding whether to take a drug, yet the headline number is identical.

That is the whole problem in one sentence. Relative measures are baseline-independent by construction, and the baseline is the single most important thing you need to know. A ratio deliberately divides it out, so the figure that travels furthest through press releases and news copy is precisely the one stripped of the decision-relevant information.

The honest translation is the absolute risk reduction: control rate minus treated rate, in percentage points. One divided by that difference gives the number needed to treat, which answers the question a reader is actually asking. How many people like me have to take this, for how long, for one of us to avoid the outcome? Every example below is that same two-step calculation on a real trial.

The arithmetic worked on a real cardiovascular trial

The cleanest recent example is the semaglutide cardiovascular outcomes trial, which randomised roughly 17,600 adults with established cardiovascular disease and a body mass index of 27 or above, none with diabetes, to semaglutide 2.4 mg weekly or placebo. The primary endpoint was a composite of cardiovascular death, non-fatal myocardial infarction and non-fatal stroke; mean follow-up was about 39.8 months, so roughly 3.3 years.

The reported hazard ratio was 0.80, with a 95 percent confidence interval of about 0.72 to 0.90. That is where the 20 percent comes from. The event rates behind it were approximately 6.5 percent on semaglutide and 8.0 percent on placebo. Subtract: 1.5 percentage points over 3.3 years. Now invert. One divided by 0.015 is 66.7, so about 67 people had to take semaglutide for three and a third years for one to avoid a cardiovascular death, heart attack or stroke. Multiply 67 by 3.3 and that is roughly 220 person-years of treatment per event prevented.

None of that makes the result unimpressive. A 1.5 point reduction on a hard composite endpoint, in a population already taking statins and antiplatelets, is a real finding, and in March 2024 the FDA added a cardiovascular risk reduction indication to the semaglutide 2.4 mg label on the strength of it. The point is that 20 percent and 1.5 percentage points are the same result, and only one of them can be weighed against cost, burden and side effects.

Why the same 20 percent is worth wildly different amounts

Hold the relative reduction at 20 percent and vary only the baseline. From 40 percent you get 32 percent: an 8 point absolute reduction, a number needed to treat of about 13. From 20 percent you get 16 percent, 4 points, and 25. From 8 percent, 6.4 percent, 1.6 points, and 63. From 2 percent, 1.6 percent, 0.4 points, and 250. From 0.5 percent, 0.4 percent, a tenth of a point, and 1,000.

Same drug, same effect, same headline, and the number who must be treated to help one of them varies by a factor of nearly eighty. This is why indications are restricted by risk stratum rather than by mechanism: a treatment can be excellent in secondary prevention and close to worthless in low-risk primary prevention while producing the identical relative figure in both.

It is also the error made when a result is carried to someone unlike the people studied. The semaglutide trial enrolled people who had already had a heart attack, a stroke or symptomatic peripheral arterial disease. Someone with excess weight and no cardiovascular history has a far lower three-year event rate, so the correct operation is to multiply the 20 percent against their baseline, not the trial's. The reverse error exists too: a modest average figure understates what the drug does for the sickest enrolled subgroup, who get the largest absolute benefit from the same unchanged ratio.

Number needed to treat only means something with a clock attached

A number needed to treat is not a property of a drug. It is a property of a drug, a population, an endpoint and a time horizon, and dropping any one makes it meaningless. Sixty-seven is not semaglutide's number needed to treat; it is the figure for that composite endpoint, in that population, over about 3.3 years. Quote it without the horizon and you have invented a statistic.

Time matters because absolute risk accumulates while relative risk usually does not. If the hazard is roughly constant and events stay uncommon, doubling follow-up roughly doubles the absolute reduction and halves the number needed to treat. Over one year the semaglutide figure would be nearer 220 than 67; over ten years, if the effect persisted, far smaller. Nobody has run that trial, so the ten-year number is an extrapolation, not a finding.

This is why person-years of treatment is often the more useful unit. Roughly 220 person-years per event prevented can be set against the injections, side effects, monitoring and expenditure those same 220 years generate.

Hazard ratio, risk ratio and odds ratio are three different 20 percents

Compute all three from that trial's own numbers. The crude risk ratio is 6.5 divided by 8.0, about 0.81, so a 19 percent reduction. The odds ratio, comparing 6.5 against 93.5 with 8.0 against 92.0, comes out near 0.80, a 20 percent reduction. The reported hazard ratio, which uses the timing of every event rather than final counts alone, was 0.80. All three land within about a percentage point here.

They agree because events were uncommon. When the outcome is rare the odds ratio approximates the risk ratio well; when it is common it does not, and always looks like the bigger effect. A control rate of 40 percent against a treated rate of 32 percent is a risk ratio of 0.80 but an odds ratio of about 0.71, which a careless write-up renders as a 29 percent reduction. That gap is pure artefact.

The hazard ratio has its own subtlety: it averages the instantaneous event rate among those still at risk and assumes that ratio is roughly constant over time. When curves separate late, as they often do in cardiovascular prevention, it compresses a story in which early benefit was near zero and later benefit larger. None of the three is wrong; only the absolute event rates let you check which one a headline used.

Composite endpoints hide which component actually moved

That trial's primary endpoint bundled three events together. This is standard practice because it raises the event count and therefore the power, but it means the headline reduction is an average across components that may not have behaved alike.

Here the reduction was driven substantially by non-fatal myocardial infarction, hazard ratio in the region of 0.72. Non-fatal stroke showed essentially no separation, its confidence interval comfortably spanning 1.0. Cardiovascular death moved favourably but its interval touched or crossed 1.0, so no mortality benefit was established on that component alone. A reader assuming the 20 percent applied uniformly to all three would be wrong on two of them.

This matters across trials, because different programmes bundle different things. A composite including hospitalisation for unstable angina or revascularisation accrues more events than one restricted to death, infarction and stroke, and those softer components are more vulnerable to ascertainment differences between arms when side effects can unblind participants. Find out what went into the composite before comparing relative figures.

The same arithmetic applies to harms, and usually is not done

Number needed to harm is the mirror image: one divided by the absolute increase in risk. It is rarely reported, producing an asymmetry in which benefits arrive as inflated relative figures and harms as reassuring absolute ones, or as nothing at all.

In the semaglutide cardiovascular trial, discontinuation for adverse events ran at roughly 17 percent on drug against roughly 8 percent on placebo: an absolute difference near 9 percentage points, a number needed to harm of about twelve. Set that beside a number needed to treat of 67 over the same period and the trade-off becomes visible. For every person who avoided a cardiovascular event, roughly five stopped the drug because they could not tolerate it. The counterweight is that serious adverse events were not more common on semaglutide; they were slightly less, around 33 percent against 36 percent.

The classic demonstration outside this field is the Women's Health Initiative combined hormone therapy arm, where a 26 percent relative increase in invasive breast cancer was, in absolute terms, about 38 cases per 10,000 women per year against about 30. Eight extra cases per 10,000 per year is a number needed to harm near 1,250 a year. Both are accurate; the relative one dominated a decade of coverage.

Converting the incretin outcome trials into comparable numbers

The liraglutide cardiovascular outcomes trial randomised about 9,340 people with type 2 diabetes at high cardiovascular risk over a median of roughly 3.8 years. The primary composite occurred in about 13.0 percent against 14.9 percent on placebo, a hazard ratio of 0.87. Subtract: 1.9 percentage points, a number needed to treat of about 53. Cardiovascular death was about 4.7 percent against 6.0 percent, 1.3 points, and a number needed to treat near 77. That trial supported adding a cardiovascular indication to the liraglutide 1.8 mg label in 2017.

The earlier semaglutide trial in type 2 diabetes, a two-year pre-approval safety study of roughly 3,300 participants, reported the composite in about 6.6 percent against 8.9 percent: 2.3 points, a number needed to treat near 44 over two years, from a hazard ratio around 0.74. It reads as the strongest of the three and is also the smallest and shortest, designed to rule out harm rather than demonstrate benefit, with a correspondingly wide confidence interval. Precision and effect size are different things.

Tirzepatide is where comparison gets genuinely hard. Its cardiovascular outcomes trial in type 2 diabetes used dulaglutide as an active comparator rather than placebo and reported a hazard ratio near 0.92 with an interval reaching about 1.0. No placebo-relative absolute benefit can be read off that design, because the control arm was itself receiving a drug with an established cardiovascular effect. The placebo-controlled obesity morbidity and mortality trial is still running. Lined up, the lesson is not that one drug beats another: these trials differ in population, comparator, endpoint and duration, and converting each to an absolute reduction mainly makes visible why the comparison should not be made.

Why relative measures get reported at all

They are not a conspiracy. Relative effects are often more stable across populations than absolute ones, which makes them the sensible unit for pooling studies in a meta-analysis and for checking consistency across subgroups; a forest plot of absolute differences across trials with different baseline risks would be mostly noise. The scale also has a legitimate biological reading: if a mechanism removes a fixed proportion of a pathway's contribution to events, constancy across risk strata is what you would expect, and departures from it are informative.

The failure is omission rather than the measure itself. A trial report giving event counts in both arms lets any reader reconstruct everything: the absolute difference, the number needed to treat, both relative measures. A press release giving only the percentage lets a reader reconstruct nothing. When the control group event rate appears nowhere in a piece of coverage, that absence is itself the finding.

Reading a risk headline in ninety seconds

Find the control group event rate. If it is not in the article, look in the abstract, then the results table. If it exists nowhere, no number in the headline can be interpreted. Then subtract the treated rate from the control rate for the absolute difference in percentage points, and divide one by that decimal for the number needed to treat.

Then ask four questions. Over what period, because the number needed to treat is meaningless without one. In whom, because the enrolled population's baseline risk is what the ratio was multiplied against and may be nothing like yours. On what endpoint, and if it is a composite, which component moved. And what is the number needed to harm, computed the same way from the adverse event rates in the same table.

Doing this to the semaglutide trial turns a 20 percent headline into a usable sentence: in adults who already had cardiovascular disease and excess weight, treating about 67 for a bit over three years prevented one cardiovascular death, heart attack or stroke, while about one in twelve stopped for side effects. Longer, less quotable, and the only version that supports a decision.

What we still don't know

Every claim above has a limit. These are the questions the current evidence does not answer.

  • Whether the relative reduction seen in adults with established cardiovascular disease holds at the same magnitude in primary prevention, where the absolute benefit would be several times smaller and no adequately powered outcome trial has reported.
  • Whether the cardiovascular benefit of the incretins tracks weight lost, glycaemic change, or a weight-independent vascular effect, which decides whether absolute benefit can be predicted from baseline cardiovascular risk alone.
  • Whether the absolute risk reduction keeps accumulating at a constant rate beyond about four years or plateaus, the difference between a number needed to treat that keeps falling with time and one that does not.
  • What the placebo-controlled absolute cardiovascular benefit of tirzepatide is, given that its completed outcome trial used an active comparator and the obesity morbidity and mortality trial has not reported.
  • Whether the roughly nine point excess in discontinuation seen under trial conditions is higher or lower in routine care, which shifts the real-world number needed to treat far more than any adjustment to the hazard ratio.

Common questions

Is a relative risk reduction ever the more useful number?
Yes, for two specific jobs. Pooling results across trials with different baseline risks works better on the relative scale, because absolute differences vary with baseline while relative ones are often roughly constant. Checking whether a treatment behaves consistently across subgroups also relies on relative measures. What a ratio cannot do is tell an individual how much benefit to expect, because that needs their own baseline risk.
How do I calculate absolute risk reduction from a news article?
You need the event rate in each group. Subtract the treated rate from the control rate and the result, in percentage points, is the absolute risk reduction. Divide one by that figure as a decimal for the number needed to treat. Using the semaglutide cardiovascular trial: 8.0 percent minus 6.5 percent is 1.5 points, and one divided by 0.015 is about 67 people treated across the trial's roughly 3.3-year follow-up. If no control event rate is given, the calculation cannot be done.
Why do the hazard ratio and the risk ratio give different percentages?
They measure different things. A risk ratio compares the proportion of people who had an event by the end of follow-up. A hazard ratio compares the instantaneous rate of events among those still event-free, averaged across follow-up, and assumes that ratio is roughly stable over time. An odds ratio compares odds rather than proportions and always exaggerates relative to the risk ratio, mildly when events are rare and substantially when they are common.
Does a number needed to treat of 67 mean the drug failed for the other 66 people?
No, and this is a common misreading. The number needed to treat is a population-level accounting figure, not a claim that 66 specific people received nothing. Nobody can identify in advance who avoided an event, and the others may have gained on outcomes the trial was not powered to detect, or nothing at all. Read it as the cost side of a trade: 67 people accept the burden and side effects so that one event is prevented among them over the stated period.
Why is number needed to harm reported so much less often than number needed to treat?
Partly because harms are usually secondary or safety endpoints rather than a trial's headline result, and partly because the incentives in coverage run one way. The consequence is an asymmetry: benefits reach the reader as a relative percentage that sounds large, while harms arrive as an absolute figure that sounds small, or are omitted. The correction is to compute both from the same table.
Can I apply a trial's 20 percent reduction to my own risk?
Only if you first establish your own baseline risk over the same period, and only if you resemble the people enrolled. Multiply your baseline by the relative reduction to estimate the absolute benefit. That trial enrolled adults with established cardiovascular disease and no diabetes, a group with a high three-year event rate; someone with a much lower baseline would derive a proportionally smaller benefit. Whether the relative effect even holds outside the enrolled population is untested. This is general reasoning about evidence, not medical advice.

What this is based on

Named sources, with what each one actually showed. We link live literature searches rather than a frozen citation list, so you can check the current record yourself.

  1. SELECT semaglutide cardiovascular outcomes trial — In about 17,600 adults with established cardiovascular disease and overweight or obesity but not diabetes, semaglutide 2.4 mg weekly reduced the composite of cardiovascular death, non-fatal myocardial infarction and non-fatal stroke from roughly 8.0 percent to 6.5 percent over a mean 39.8 months, a hazard ratio of 0.80. find on PubMed
  2. LEADER liraglutide cardiovascular outcome trial — In about 9,340 adults with type 2 diabetes at high cardiovascular risk followed for a median of roughly 3.8 years, the primary composite occurred in about 13.0 percent on liraglutide against 14.9 percent on placebo, a hazard ratio of 0.87. find on PubMed
  3. SUSTAIN-6 semaglutide cardiovascular safety trial — A two-year pre-approval trial in roughly 3,300 adults with type 2 diabetes reported the primary cardiovascular composite in about 6.6 percent on semaglutide against 8.9 percent on placebo, a hazard ratio near 0.74, from a study designed to exclude harm rather than demonstrate benefit. find on PubMed
  4. SURPASS-CVOT tirzepatide cardiovascular outcomes trial — Compared tirzepatide against dulaglutide rather than placebo in adults with type 2 diabetes and atherosclerotic disease, reporting a hazard ratio near 0.92 with a confidence interval reaching approximately 1.0, a design from which no placebo-relative absolute benefit can be derived. find on PubMed
  5. SURMOUNT-MMO tirzepatide morbidity and mortality trial — The placebo-controlled cardiovascular outcomes trial of tirzepatide in obesity without diabetes, still ongoing, and the study that would supply a directly comparable absolute risk reduction. find on PubMed
  6. SOUL oral semaglutide cardiovascular outcomes trial — Reported a reduction in major adverse cardiovascular events with oral semaglutide in adults with type 2 diabetes and cardiovascular or kidney disease, with a hazard ratio in the region of 0.86. find on PubMed
  7. FDA approval of a cardiovascular risk reduction indication for semaglutide 2.4 mg — In March 2024 the US regulator added an indication for reducing the risk of major adverse cardiovascular events in adults with cardiovascular disease and overweight or obesity, on the basis of the SELECT result. find on PubMed
  8. FDA cardiovascular indication for liraglutide 1.8 mg — In 2017 the liraglutide label for type 2 diabetes gained an indication for reducing major adverse cardiovascular events, based on the LEADER trial. find on PubMed
  9. Women's Health Initiative oestrogen plus progestin trial — Reported a hazard ratio of about 1.26 for invasive breast cancer, corresponding in absolute terms to roughly 38 versus 30 cases per 10,000 women per year, the standard illustration of a relative increase dominating coverage of a small absolute one. find on PubMed
  10. CONSORT statement for reporting randomised trials — Requires trial reports to present results for each group with the effect size and its precision, and recommends giving both absolute and relative effect measures for binary outcomes. find on PubMed
  11. Cochrane Handbook for Systematic Reviews of Interventions — Sets out why relative effect measures are preferred for pooling across studies and why absolute measures must be recomputed against an assumed baseline risk before results are interpreted for a population. find on PubMed
  12. GRADE summary of findings tables — Require evidence summaries to express effects as absolute risk differences at specified baseline risks alongside relative estimates, precisely to stop baseline-independent figures being read as decision-relevant. find on PubMed
  13. Number needed to treat: derivation and limitations — Establishes that the number needed to treat is the reciprocal of the absolute risk reduction and is uninterpretable without a stated time horizon, population and endpoint. find on PubMed
  14. Framing effects of relative versus absolute risk presentation — Experimental work showing that clinicians and patients consistently rate the same treatment as more worthwhile when its effect is presented in relative rather than absolute terms. find on PubMed

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Terms used in this article

Absolute vs Relative Risk Reduction
Absolute risk reduction is the arithmetic difference between the event rates in two groups, while relative risk reduction expresses that same difference as a proportion of the control group's risk.
Number Needed to Treat (NNT)
Number needed to treat is the number of people who must receive a treatment for one additional person to benefit, the reciprocal of the absolute risk reduction over a stated period.
Primary vs Secondary Endpoint
The primary endpoint is the single prespecified outcome a trial is powered and statistically budgeted for, while secondary endpoints are additional measures that support interpretation but cannot replace it.
Body Mass Index (BMI)
Body mass index is weight in kilograms divided by height in metres squared, a population screening statistic for adiposity that is also used as a drug eligibility threshold.
Placebo and Placebo Control
A placebo is an inactive intervention matched to the real one in appearance and route, used as a control arm so that improvement caused by the drug can be separated from improvement that would occur anyway.
Confidence Interval (CI)
A confidence interval is the range produced by a procedure that would capture the true value in a stated percentage of repeated samples, not the probability that this particular interval contains it.
Hazard Ratio (HR)
A hazard ratio compares the instantaneous rate of events between two groups among those still at risk, averaged over follow-up, and it is not a ratio of probabilities.
Composite Endpoint
A composite endpoint counts several distinct outcomes together as one event, raising the event rate and statistical power at the cost of blurring which component actually moved.
Adverse Drug Reaction (ADR)
An adverse drug reaction is a noxious, unintended response to a medicine given at normal doses, and unlike an adverse event the term carries a causal judgement inside it.
Risk Ratio (Relative Risk)
A risk ratio divides the probability of an outcome in one group by the probability in another, so 1 means no difference and the distance from 1 is the relative effect, whatever the underlying risks.
Odds Ratio (OR)
An odds ratio divides the odds of an outcome in one group by the odds in another, where odds are events divided by non-events, and it exaggerates the risk ratio whenever the outcome is common.
Number Needed to Harm (NNH)
Number needed to harm is the number of patients who must receive a treatment for one additional person to suffer a specified adverse outcome, the reciprocal of the absolute risk increase.

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