VerifiedEvidence: highv1.0.0

Number Needed to Treat

The number of patients who would need to receive a treatment for one additional patient to experience a beneficial outcome.

Last reviewedDarrin Baines IP Ltd

Concept Architecture

Concept


Theoretically, Number Needed to Treat (NNT) is an epidemiological and clinical measure that quantifies the number of patients who must receive an intervention instead of a comparator for one additional beneficial outcome to occur over a specified period. It represents the reciprocal of the Absolute Risk Reduction (ARR) and provides an absolute measure of treatment effectiveness. The concept is founded on absolute risk comparison and exists to express treatment benefit in a clinically meaningful form for evidence-based decision-making.

Mathematically, the Number Needed to Treat is represented as the reciprocal of the Absolute Risk Reduction between treatment and control groups. Because it is based on an absolute difference in event risk, NNT is expressed as the number of patients requiring treatment to achieve one additional favourable outcome. Smaller NNT values indicate greater treatment effectiveness, whereas larger values indicate smaller absolute benefit.

In practice, Number Needed to Treat is calculated from randomised controlled trials, observational studies and meta-analyses using absolute event risks. It is widely applied in clinical guideline development, health technology assessment and health economic evaluation to compare intervention effectiveness, communicate treatment benefit and support benefit-risk assessments alongside measures such as the Number Needed to Harm.

Purpose


Used to quantify the absolute effectiveness of healthcare interventions, compare treatment benefits, communicate clinical impact and support evidence-based and health economic decision-making.


Mathematical Formulae

Primary Formula

NNT = 1 / ARR

where:

  • NNT = number needed to treat
  • ARR = absolute risk reduction

Supporting Formulae

ARR = Risk?? ? Risk?

Risk = Events / Population

NNT = 1 / (Risk?? ? Risk?)

where:

  • Risk?? = risk in the control group
  • Risk? = risk in the treatment group

Related Mathematical Methods

  • Absolute Risk Reduction
  • Relative Risk
  • Risk Difference
  • Number Needed to Harm
  • Number Needed to Screen
  • Attributable Risk
  • Benefit-Risk Assessment

Example


A clinical trial reports that 15% of patients receiving standard care experience disease progression compared with 10% receiving a new treatment.

Absolute Risk Reduction:

ARR = 0.15 ? 0.10 = 0.05

Number Needed to Treat:

NNT = 1 / 0.05 = 20

Twenty patients must receive the new treatment instead of standard care to prevent one additional case of disease progression during the study period.


Excel Implementation

FunctionExample FormulaHealth Economics Application
Subtraction=B2-C2Calculates the Absolute Risk Reduction from control and treatment event risks.
Division=1/(B2-C2)Calculates the Number Needed to Treat.
ROUNDUP=ROUNDUP(1/(B2-C2),0)Rounds the Number Needed to Treat up to the nearest whole patient for reporting.
IF=IF(B2>C2,ROUNDUP(1/(B2-C2),0),"No treatment benefit")Calculates NNT only when treatment reduces event risk.

VBA (Optional)


A VBA macro can automatically calculate Number Needed to Treat values, confidence intervals and comparative effectiveness summaries across multiple clinical studies.


Sources

  • Cook RJ, Sackett DL. The number needed to treat: a clinically useful measure of treatment effect. BMJ. 1995;310:452?454.
  • Altman DG. Confidence intervals for the number needed to treat. BMJ. 1998;317:1309?1312.
  • Altman DG, Andersen PK. Calculating the number needed to treat for trials where the outcome is time to an event. BMJ. 1999;319:1492?1495.
  • Drummond MF, et al. Methods for the Economic Evaluation of Health Care Programmes. 4th ed.
  • NICE. Health Technology Evaluation Manual.

Library

Publications

1
  • Book

    Statistical Analysis of Cost-Effectiveness Data — Willan & Briggs, 1st Edition ed., 2006 (John Wiley & Sons)

    A synthesis of statistical methods for analysing cost-effectiveness data, including net-benefit regression, confidence intervals for the ICER, cost-effectiveness acceptability curves, and covariate adjustment. Part of the Wiley Statistics in Practice series.

Frequently Asked Questions (6)

  • What is the number needed to treat?

    The number of patients who would need to receive a treatment for one additional patient to experience a beneficial outcome.

    Source: Cook & Sackett 1995

  • What does the number needed to treat tell a clinician about benefit?

    The number needed to treat is how many patients must receive a treatment for one extra person to gain the beneficial outcome. It tells a clinician the effort a benefit costs in tangible terms, so a number needed to treat of five means one in five treated benefits, while a hundred means the great majority gain nothing. This concrete framing conveys the size of a benefit more vividly than a percentage, and it aids comparison between treatments. Expressing benefit as people treated per success is its value. Sackett and colleagues (1991) describe this measure.

    Source: Sackett et al. 1991

  • How is the number needed to treat calculated?

    The number needed to treat is calculated as the reciprocal of the absolute risk reduction, that is one divided by the difference in the risk of the outcome between the control and treatment groups. If a treatment reduces the risk of an adverse outcome by a certain absolute amount, the number needed to treat is one divided by that amount. So the number needed to treat is calculated from the absolute risk reduction, taking its reciprocal, which converts the difference in outcome risk between groups into the number of patients who must be treated for one additional patient to benefit, linking it directly to the absolute measure of effect.

    Source: Cook & Sackett 1995

  • How is the number needed to treat interpreted?

    The number needed to treat is interpreted as the number of patients who must be treated for one extra beneficial outcome: a small number, such as a few patients, indicates an effective treatment where benefit comes readily, while a large number indicates that many must be treated for one to benefit. So the number needed to treat is interpreted as how many treatments yield one additional good outcome, with smaller values indicating greater benefit, and it is weighed against harms and costs, since a treatment with a favourable number needed to treat may still be judged in light of its number needed to harm and its expense.

    Source: Cook & Sackett 1995

  • Why is the number needed to treat useful?

    The number needed to treat is useful because it translates a treatment's effect into an intuitive figure that patients and clinicians can grasp, conveying the practical benefit better than relative measures, which can seem impressive even when the absolute benefit is small. It reflects the baseline risk and links directly to the absolute risk reduction. So the number needed to treat is useful for communicating and judging the real-world benefit of a treatment, helping to weigh benefit against harm and cost, since knowing how many patients must be treated for one to benefit gives a clearer sense of value than a relative risk reduction alone.

    Source: Cook & Sackett 1995

  • What are the limitations of the number needed to treat?

    The limitations of the number needed to treat include that it depends on the baseline risk and the time period, so it varies between populations and durations and is not a fixed property of a treatment; that it requires a reliable estimate of the absolute risk reduction; and that a single figure does not capture harms or the severity of outcomes. So the number needed to treat is interpreted with attention to the baseline risk, time frame, and population to which it applies, and used alongside the number needed to harm and other information, since comparing numbers needed to treat across settings requires comparable baseline risks and the measure reflects only one benefit at a time.

    Source: Cook & Sackett 1995

Trust Record

Verified by Dr Darrin Baines

British health economist

Professional identity: darrinbaines.org

Verification date: 9 Dec 2025

Content version: 1.0.0

Canonical Identity

Term code
HE-ES-RM-018

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