Number Needed-to-Treat
A Number Needed-to-Treat is the number of patients that must be treated to prevent one adverse outcome or for one patient to benefit.
- AKA: NNT.
- Context:
- Counter-Example(s):
- See: Probability, Placebo, Control Event Rate, Experimental Event Rate, Absolute Risk Reduction, Relative Risk Reduction.
References
2016
- (Wikipedia, 2016) ⇒ https://www.wikiwand.com/en/Number_needed_to_treat Retrieved 2016-07-24
- The number needed to treat (NNT) 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 (e.g. the number of patients that need to be treated for one to benefit compared with a control in a clinical trial). 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.
NNT is similar to number needed to harm (NNH), where NNT usually refers to a therapeutic intervention and NNH to a detrimental effect or risk factor.
- The number needed to treat (NNT) 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 (e.g. the number of patients that need to be treated for one to benefit compared with a control in a clinical trial). 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.
2008
- (Irwig et al., 2008) ⇒ Irwig, L., Irwig, J., Trevena, L., & Sweet, M. (2008). Relative risk, relative and absolute risk reduction, number needed to treat and confidence intervals. http://www.ncbi.nlm.nih.gov/books/NBK63647/
- How do you interpret the results of a randomised controlled trial? A common measure of a treatment is to look at the frequency of bad outcomes of a disease in the group being treated compared with those who were not treated. For instance, supposing that a well-designed randomised controlled trial in children with a particular disease found that 20 per cent of the control group developed bad outcomes, compared with only 12 per cent of those receiving treatment. Should you agree to give this treatment to your child? Without knowing more about the adverse effects of the therapy, it appears to reduce some of the bad outcomes of the disease. But is its effect meaningful?
This is where you need to consider the risk of treatment versus no treatment. In healthcare, risk refers to the probability of a bad outcome in people with the disease.
Absolute risk reduction (ARR) – also called risk difference (RD) – is the most useful way of presenting research results to help your decision-making. In this example, the ARR is 8 per cent (20 per cent - 12 per cent = 8 per cent). This means that, if 100 children were treated, 8 would be prevented from developing bad outcomes. Another way of expressing this is the number needed to treat (NNT). If 8 children out of 100 benefit from treatment, the NNT for one child to benefit is about 13 (100 ÷ 8 = 12.5).
For technical reasons, some other measures are often used. The relative risk (RR) of a bad outcome in a group given intervention is a proportional measure estimating the size of the effect of a treatment compared with other interventions or no treatment at all. It is the proportion of bad outcomes in the intervention group divided by the proportion of bad outcomes in the control group. In this hypothetical case, the RR is 0.6 (12 per cent ÷ 20 per cent = 0.6).
When a treatment has an RR greater than 1, the risk of a bad outcome is increased by the treatment; when the RR is less than 1, the risk of a bad outcome is decreased, meaning that the treatment is likely to do good. For example, when the RR is 2.0 the chance of a bad outcome is twice as likely to occur with the treatment as without it, whereas an RR of 0.5 means that the chance of a bad outcome is twice as likely to occur without the intervention. When the RR is exactly 1, the risk is unchanged. For example, a report may state ‘The relative risk of blindness in people given drug T was 1.5’. This shows that the drug increased the risk of blindness. Another measure that is used is the odds ratio. For practical purposes, assume that the odds ratio is the same as the relative risk. Sometimes the outcome is a good one and the interpretation of relative risk is the opposite of what we have just outlined (...)
- How do you interpret the results of a randomised controlled trial? A common measure of a treatment is to look at the frequency of bad outcomes of a disease in the group being treated compared with those who were not treated. For instance, supposing that a well-designed randomised controlled trial in children with a particular disease found that 20 per cent of the control group developed bad outcomes, compared with only 12 per cent of those receiving treatment. Should you agree to give this treatment to your child? Without knowing more about the adverse effects of the therapy, it appears to reduce some of the bad outcomes of the disease. But is its effect meaningful?