Application of Number Needed to Treat (NNT) as a Measure of Treatment Effect in Respiratory Medicine
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Application of Number Needed to Treat (NNT) as a Measure of Treatment Effect in Respiratory Medicine Mario Cazzola Unit`a di Pneumologia ed Allergologia e Settore di Farmacologia Clinica Respiratoria, Dipartimento di Pneumologia, Ospedale ad Alta Specializzazione A. Cardarelli, Naples, Italy
Abstract
Presentation of clinical data can have a profound effect on treatment decisions, and there is a need for measures that are objective, have clinical relevance, and are easily interpreted. Relative risk is often used to summarize treatment comparisons, but does not account for variations in baseline risk profiles and does not convey information on absolute sizes of treatment effects. Absolute risk reduction gives this information, but the data are dimensionless and abstract, and lack a direct connection with the clinical environment. The number needed to treat, or NNT, has been developed to address this issue. NNT is the reciprocal of the absolute risk reduction associated with an intervention, and may also be calculated as 100 divided by the absolute risk reduction expressed as a percentage. The result is the number of patients who would have to receive treatment for one of them to benefit or to avoid an adverse outcome over a given period of time. Since its introduction, the concept of NNT has been expanded to include number needed to harm (NNH), which illustrates adverse events or other undesirable outcomes associated with treatment, and the epidemiologic tool of number needed to screen. NNT has been used to describe treatment effects from many clinical trials. A recent example illustrates benefit of inhaler therapy combining a long-acting β2-agonist (LABA) and corticosteroid for COPD over treatment with LABA alone. NNT has also been extended to systematic reviews and meta-analyses, where it has been used to rank different treatments where baseline profiles, treatment outcomes and time periods under examination are similar. NNT is therefore a concise and easily understood tool for quantifying treatment efficacy, particularly when applying trial results to the clinic setting.
It has been clear, since publications in the early to mid-1990s, that the results of clinical studies, and the method of reporting trial results, influence clinicians, policy makers and patients.[1-4] Importantly, perception of the magnitude of any intervention can be influenced markedly by the way in which measurements of effect are reported. As an example, Naylor et al.[4] found that clinicians’ views of therapeutic effectiveness varied according to the endpoint used. For example, ratings were lower when results were reported as absolute versus relative differences.[4] In another piece of research published at around the same time, 108 of 235 prescribers gave differing responses to a questionnaire when research results were presented in two different ways, and 90% of the 108 prescribers indicated that they would be more strongly inclined to arrive at a
treatment decision
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