Interpretation of treatment effects in periodontal research: a note on the number needed to treat.
Year: 2008
Type: article
Abstract: In clinical research, investigators are interested in determining whether new interventions are safer and more effective than standard therapies. In clinical practice, dental practitioners must interpret research findings to determine whether new therapeutic approaches should be incorporated into practice. The effect of a new treatment, relative to that of the standard of care or a placebo control, can be presented in many different ways using a variety of summary statistics. For example, in periodontal research, the effect of an intervention relative to that of a control may be based on differences between the study groups in the proportion of sites with ≥ 2 mm attachment loss (risk difference), ratios of the proportions of sites with ≥ 2 mm attachment loss (relative risk), or differences in mean attachment loss. Another measure of treatment effect, used to summarize the clinical benefit of a treatment, is the number needed to treat (NNT).1,2 In the context of dental research, NNT is defined as the number of sites that must be treated with the intervention to avoid one additional site with progressive disease compared to the control. The statistical and clinical significance of the estimated treatment effect, the safety profile and the feasibility of delivering the intervention are all used to determine whether an investigational treatment should be adopted into practice.
Many patients with periodontitis will have only a small number of sites with active disease demonstrating disease progression over the study period and hence only a small number of sites that may be responsive to treatment. In such patient populations, rates of disease progression and mean changes in measures such as probing depth and clinical attachment level over the treatment period are very low. It is important to understand how such low rates of disease progression influence estimates of treatment effects. This paper builds on the existing NNT literature by illustrating the influence of low disease-progression rates on calculations of NNT in periodontal research.
Source: PubMed
Authors: Julie A Stoner, Jeffrey B Payne
Institution: University of Oklahoma Health Sciences Center
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W132344938