Methodology for determining minimally clinically important differences in acute pain intensity with the double stopwatch technique

Abstract

Minimum clinically important differences (MCIDs) in acute pain intensity have not been well established. Conventional approaches for estimating MCIDs require an independent reference scale, with a threshold that must be presumed to accurately classify meaningful change in pain for all study participants, to serve as an anchor. The double stopwatch technique is the gold standard for measuring the time to meaningful relief, where participants actively press the second stopwatch when they experience pain relief that is meaningful to them. This technique eliminates the problem of misclassification with arbitrary anchors at a single time point, but the censored nature of the data is not amenable for determining MCIDs using standard methods. We propose a stopwatch-based MCID methodology that employs the double stopwatch technique to identify individualized thresholds for meaningful change in pain. This approach enables direct classification of changes in pain for each participant based on whether they perceived the change as meaningful and whether it exceeded the study cut-off being tested. Pain values of participants who do not achieve meaningful relief are incorporated into the analysis to address censoring and avoid bias. The performance (e.g., sensitivity, specificity) of different thresholds to serve as an MCID can be estimated using standard approaches with variance estimates derived by cluster bootstrapping. The advantages of the stopwatch-based MCID methodology are illustrated relative to a conventional approach using data from a randomized trial in third molar extraction. PERSPECTIVE: This article describes a methodology for determining MCIDs using the double stopwatch technique, the gold standard for assessing meaningful changes in acute pain. This methodology can be used to establish MCIDs in different acute pain settings, providing a useful basis to evaluate the meaningfulness of clinical trial results.

Keywords: Acute pain; Clinical trials; Minimum clinically important difference; Pain measurement; Statistical analysis.