A Proposal to Use Confidence Intervals for Visual Analog Scale Data for Pain Measurement to Determine Clinical Significance

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Abstract

Visual analog scales (VAS) ranging from 0 cm (no pain) to 10 cm (worst imaginable pain) are used widely for pain measurement, but various investigators have not treated these data consistently. Conventional statistical tests of such data, although evaluating the “statistical significance” may obscure the clinical value of a treatment. On the other hand, confidence intervals (CIs) can illuminate both statistical and clinical importance. CIs give a range of values based on the observed data which contain, with a specified probability, a true but unknown variable typifying a population. We reviewed 112 articles published recently in anesthesia journals for statistical reporting of VAS data. Of the 112 articles, only two used CIs to report mean pain scores and one used CIs to report differences in median pain scores between the study groups. Only two articles presented 95% CI for the mean pain scores graphically. Analgesic techniques that produce VAS values in the range of 0–3 have been reported to represent adequate analgesia. A graphical method using CIs is proposed that allows ready interpretation of VAS data. With this approach, one evaluates whether the 95% CI for the mean pain score in a group during a particular period lies entirely within the zone defined as “analgesic success” (0–3). Such an analysis allows a visual assessment of whether a particular technique would produce clinically important effects in the population at large. This approach seems to provide more information than the use of conventional hypothesis testing in the interpretation of VAS data for pain measurement.

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