Standard tests using individual patient slopes for linear data sets and averaged measures for others are highly sensitive in detecting treatment effects.
Because acute procedural pain tends to increase with procedure time, assessments of pain management strategies must take that time relationship into account. Statistical time-course analyses are, however, complex and require large patient numbers to detect differences. The current study evaluated the abilities of various single and simple composite measures such as averaged pain or individual patient pain slopes to detect treatment effects. Secondary analyses were performed with the data from 3 prospective randomized clinical trials that assessed the effect of a self-hypnotic relaxation intervention on procedural pain, measured every 10–15 minutes during vascular/renal interventions, breast biopsies, and tumor embolizations. Single point-in-time and maximal pain comparisons were poor in detecting treatment effects. Linear data sets of individual patient slopes yielded the same qualitative results as the more complex repeated measures analyses, allowing the use of standard statistical approaches (eg, Kruskal-Wallis), and promising analyses of smaller subgroups, which otherwise would be underpowered. With nonlinear data, a simple averaged score was highly sensitive in detecting differences. Use of these 2 workable and relatively simple approaches may be a first step towards facilitating the development of data sets that could enable meta-analyses of data from acute pain trials.