Number needed to treat or harm (NNT/NNH) 

A number of new readers have asked for Bandolier to revisit the calculation of NNTs. As a reminder, Bandolier gave a full account in issue 36 , and that issue is included in the second Bandolier annual (see below). Many GPs are familiar with NNTs, and a high percentage feel comfortable with understanding NNTs and explaining them to others. A report from Wessex on this is opposite. OutcomesStatistical ways to express outcomes of clinical trials include p values, odds ratios, relative risk and relative risk reduction or increase. All may have their place, but they are difficult outputs for the nonspecialist to interpret. In order to overcome this, the number needed to treat is increasingly being used. The NNT, as the name implies, is an estimate of the number of patients that would need to be given a treatment for one of them to achieve a desired outcome who would not have achieved it with control. The NNT should specify the characteristics of patients being treated, the intervention and its duration, and the outcome being measured [1].CalculationFor an analgesic trial, the NNT may be calculated very simply as:NNT = 1/(the proportion of patients with at least 50% pain relief with analgesic minus the proportion of patients with at least 50% pain relief with placebo) Taking a hypothetical example from a randomised trial:
The NNT is therefore 1/((27/50)  (10/50))The best NNT would, of course, be 1, when every patient with treatment benefited, but no patient given control benefited. Generally NNTs between 2 and 5 are indicative of effective treatments, but NNTs of 20, 50 or 100 may be useful for prophylactic treatments, like interventions to reduce death after heart attack. It all depends on the intervention and the consequences. HarmFor adverse effects, we can calculate a number needed to harm (NNH), in exactly the same way as an NNT. For an NNH, large numbers are obviously better than small numbers, because that means that the adverse effect occurs with less frequency.CommentNNTs and NNHs are a useful common currency for describing results from trials, and especially reviews and metaanalyses. They also allow us to impose our own values by describing what we believe to be a useful outcome.Reference:

previous or next story in this issue