Gauging the impact of behavior change interventions: A tutorial on the Numbers Needed to Treat

Effect size indices are valuable to research in applied psychology, but traditional measures (e.g. Cohen’s d or Pearson’s r) are limited by their ability to convey practical information about intervention effectiveness. Researchers rely on concepts such as ‘standardized mean difference’ or ‘explained variance’ to express effectiveness. Practitioners, policymakers, and lay-people prefer concepts such as frequencies. Partial solutions have been offered by rules-of-thumb (e.g. Cohen’s categories of ‘small’, ‘moderate’ and ‘large’), but such pigeon-holing is somewhat arbitrary and of little nuance. We review, and provide a tutorial on, the Numbers Needed for Treat (NNT) statistic—an effect size index that originated in the medical literature. NNT fills the communicative gap between research and practice, and is particularly suited to gauge the impact of a behavior change intervention on a population level. NNT is defined as the number of people who need to be exposed to an intervention to achieve the desired change in one more individual, relative to a control condition. NNT has informational advantages: 1) it communicates effect magnitude in a frequency-format (number of people) making the impact of an intervention on a population transparent, and 2) it considers the population behavior base-rate to estimate this number. We adapt and extend the NNT index to suit applied psychology endeavors, and argue that the measure can strengthen the translation of intervention research to practice. The statistical procedure to estimate the NNT is explained, illustrated with concrete examples, and supplemented by script and functions to calculate the index in the R - environment.