The number needed to treat is an epidemiological measure used in
communicating the effectiveness of a health-care intervention, typically a
treatment with medication. The NNT is the average number of patients who need
to be treated to prevent one additional bad outcome. It is defined as the
inverse of the absolute risk reduction. It was described in 1988. The ideal NNT
is 1, where everyone improves with treatment and no one improves with
control. The higher the NNT, the less effective is the treatment.
Variants are sometimes used for more specialized purposes. One example is
number needed to vaccinate. NNT values are time-specific. For
example, if a study ran for 5 years and another ran for 1 year the NNT values
would not be directly comparable. Derivation
NNT is the statistical inverse of the absolute risk reduction i.e. 1/absolute
risk reduction. In general, NNT is computed with respect to two treatments
A and B, with A typically the intervention and B the control. A
defined endpoint has to be specified. If the probabilities pA and pB of this
endpoint under treatments A and B, respectively, are known, then the NNT is
computed as 1/(pB – pA). NNT is a number between 1 and ∞; effective interventions
have a low NNT. A negative number would not be presented as a NNT, rather, as
the intervention is harmful, it is expressed as a number needed to harm.
The units of the aforementioned probabilities are expressed as number of
events per subject; therefore, the inverse NNH will be number of subjects
per event. Relevance
The NNT is an important measure in pharmacoeconomics. If a clinical
endpoint is devastating enough, drugs with a high NNT may still be indicated
in particular situations. If the endpoint is minor, health insurers may
decline to reimburse drugs with a high NNT. NNT is significant to consider when
comparing possible side effects of a medication against its benefits. For
medications with a high NNT, even a small incidence of adverse effects may
outweigh the benefits. Even though NNT is an important measure in a clinical
trial, it is infrequently included in medical journal articles reporting the
results of clinical trials. There are several important problems with the NNT,
involving bias and lack of reliable confidence intervals, as well as
difficulties in excluding the possibility of no difference between two
treatments or groups. Example: statins for primary prevention
For example, the ASCOT-LLA manufacturer-sponsored study addressed
the benefit of atorvastatin 10 mg in patients with hypertension but no
previous cardiovascular disease. The trial ran for 3.3 years, and during this
period the relative risk of a "primary event" was reduced by 36%. The absolute
risk reduction, however, was much smaller, because the study group did not
have a very high rate of cardiovascular events over the study period: 2.67% in
the control group, compared to 1.65% in the treatment group. Taking atorvastatin
for 3.3 years, therefore, would lead to an ARR of only 1.02%. The number needed
to treat to prevent one cardiovascular event would then be 98.04 for 3.3 years.
Worked example The relative risk is 0.25 in the example
above. It is always 1-relative risk reduction, or vice versa.
Simple examples There are a number of factors that can
affect the NNT. Let's say we have a disease, and a pill to treat the
disease, that should work over the course of a week.
PA is the probability of still having the disease after taking the pill. The
experimental group. PB is the probability of still having
the disease even though you didn't take the pill. This is the control group, who
probably got a placebo pill instead of the real pill.
See also Number needed to harm – the converse for
side-effects References
External links EBEM's Calculator for NNT
Online Calculator for NNT Centre for Evidence Based Medicine: EBM
Tools: Critical Appraisal: NNT Table of NNTs for various treatments
NNT calculator by The Garvan Institute of Medical Research