• Pradeep Suri, Patrick J Heagerty, Anna Korpak, Mark P Jensen, Laura S Gold, Kwun C G Chan, Andrew Timmons, Janna Friedly, Jeffrey G Jarvik, and Aaron Baraff.
• Seattle Epidemiologic Research and Information Center, VA Puget Sound Health Care System, Seattle, Washington; Division of Rehabilitation Care Services, VA Puget Sound Health Care System, Seattle, Washington; Clinical Learning, Evidence, and Research Center, University of Washington, Seattle, Washington; Department of Rehabilitation Medicine, University of Washington, Seattle, Washington. Electronic address: pradeep.suri@va.gov.
• J Pain. 2023 Feb 1; 24 (2): 332344332-344.

AbstractThe 0 to 10 numeric rating scale of pain intensity is a standard outcome in randomized controlled trials (RCTs) of pain treatments. For individuals taking analgesics, there may be a disparity between "observed" pain intensity (pain intensity with concurrent analgesic use) and pain intensity without concurrent analgesic use (what the numeric rating scale would be had analgesics not been taken). Using a contemporary causal inference framework, we compare analytic methods that can potentially account for concurrent analgesic use, first in statistical simulations, and second in analyses of real (non-simulated) data from an RCT of lumbar epidural steroid injections. The default analytic method was ignoring analgesic use, which is the most common approach in pain RCTs. Compared to ignoring analgesic use and other analytic methods, simulations showed that a quantitative pain and analgesia composite outcome based on adding 1.5 points to pain intensity for those who were taking an analgesic (the QPAC1.5) optimized power and minimized bias. Analyses of real RCT data supported the results of the simulations, showing greater power with analysis of the QPAC1.5 as compared to ignoring analgesic use and most other methods examined. We propose alternative methods that should be considered in the analysis of pain RCTs. PERSPECTIVE: This article presents the conceptual framework behind a new quantitative pain and analgesia composite outcome, the QPAC1.5, and the results of statistical simulations and analyses of trial data supporting improvements in power and bias using the QPAC1.5. Methods of this type should be considered in the analysis of pain RCTs.Published by Elsevier Inc.

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