Dissecting Clinical Trials with 'Number Needed to Treat': Calculation Suggests a Study's Value to Your Patients

Clinical trials produce a mountain of data that can be difficult to interpret and apply to clinical practice. When reading about studies such as the Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE) for schizophrenia, you may wonder: [ILLUSTRATION OMITTED] * How large is the effect being measured? * Is it clinically important? * Are we dealing with a result that may be statistically significant but irrelevant for day-to-day patient care? Number needed to treat (NNT) and number needed to harm (NNH)--two tools of evidence-based medicine (EBM, Box 1 (1,2))--can help answer these questions. This article shows how to calculate NNT and NNH, then applies these tools to published results from CATIE phases 1 and 2. WHAT IS NNT? NNT helps us gauge effect size--or clinical significance. It is different from knowing if a clinical trial result is statistically significant. NNT allows us to place a number on how often we can expect to see a difference between two interventions. If we see a therapeutic difference once every 100 patients (an NNT of 100), the difference between two treatments is not of great concern under most circumstances. But if a difference in outcome is seen once in every 5 patients being treated with one intervention versus another (an NNT of 5), the result will likely influence day-to-day practice. Together with calculating a confidence interval (Box 2), (3) the NNT can help you judge the clinical significance of a statistically significant result. NNT is useful when examining differences in binary outcomes such as treatment response (yes/no), remission (yes/no), or avoidance of hospitalization (yes/no). NNT also is useful when we compare two medications' side effects. Under these circumstances, we call NNT the "number needed to harm" (NNH). CALCULATING NNT AND NNH NNT and NNH are easy to calculate: * First determine the difference between the frequencies of the outcome of interest for two interventions. * Then calculate the reciprocal of this difference. For example, let's say drugs A and B are used to treat depression, and they result in 6-week response rates of 55% and 75%, respectively. The NNT to see a difference between drug B versus drug A in terms of responders at 6 weeks can be calculated as follows: * Difference in response rates = 0.75 - 0.55 = 0.20 * NNT = 1 / 0.20 = 5. In this example, you would need to treat 5 patients with drug B instead of drug A to see 1 extra responder. If the NNT had been 5.5, you would round up to the next whole number (6) because you can't treat a fraction of a person. Interpreting the importance of NNT values is easy, too. The smaller the NNT, the larger the clinical difference between interventions; the larger the NNT, the smaller the difference. * An NNT of 100 or more usually means little difference exists between interventions for the outcome of interest. * An NNT of 2 would be hugely important and is rarely encountered. Keep in mind, however, that some NNTs may be clinically important even though they are relatively large. An NNT of 500, for example, could be important if the outcome measured is death. Similarly, relatively small NNTs may be clinically irrelevant, such as an NNT of 5 when the outcome is a mild dry mouth. Example. We can calculate the NNT (actually, NNH) for risk of new-onset diabetes mellitus attributable to second-generation antipsychotics (SGAs), using data from a study that compared diabetes rates in patients given SGAs versus conventional antipsychotics. (4) Differences in new-onset diabetes rates across [less than or equal to]25 months were 2.03%, 0.80%, 0.63%, and 0.05% for clozapine, quetiapine, olanzapine, and risperidone, respectively, versus first-generation antipsychotics (FGAs). The NNH for clozapine compared with FGAs is 1 / 0. …