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Improving Power and Accuracy in Randomized Controlled Trials of Pain Treatments by Accounting for Concurrent Analgesic Use

  • Pradeep Suri
    Correspondence
    Address reprint requests to Pradeep Suri, MD, MS, VA Puget Sound Health Care System, S-RCS-117, 1660 S. Columbian Way, Seattle, WA, 98108.
    Affiliations
    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
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  • Patrick J. Heagerty
    Affiliations
    Department of Biostatistics, University of Washington, Seattle, Washington
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  • Anna Korpak
    Affiliations
    Seattle Epidemiologic Research and Information Center, VA Puget Sound Health Care System, Seattle, Washington
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  • Mark P. Jensen
    Affiliations
    Department of Rehabilitation Medicine, University of Washington, Seattle, Washington
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  • Laura S. Gold
    Affiliations
    Clinical Learning, Evidence, and Research Center, University of Washington, Seattle, Washington

    Departments of Radiology and Neurological Surgery, University of Washington, Seattle, Washington
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  • Kwun C.G. Chan
    Affiliations
    Department of Biostatistics, University of Washington, Seattle, Washington
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  • Andrew Timmons
    Affiliations
    Seattle Epidemiologic Research and Information Center, VA Puget Sound Health Care System, Seattle, Washington
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  • Janna Friedly
    Affiliations
    Clinical Learning, Evidence, and Research Center, University of Washington, Seattle, Washington

    Department of Rehabilitation Medicine, University of Washington, Seattle, Washington
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  • Jeffrey G. Jarvik
    Affiliations
    Clinical Learning, Evidence, and Research Center, University of Washington, Seattle, Washington

    Departments of Radiology and Neurological Surgery, University of Washington, Seattle, Washington
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  • Aaron Baraff
    Affiliations
    Seattle Epidemiologic Research and Information Center, VA Puget Sound Health Care System, Seattle, Washington

    Department of Statistics, University of Washington, Seattle, Washington
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Published:October 07, 2022DOI:https://doi.org/10.1016/j.jpain.2022.09.017

      Highlights

      • Not accounting for analgesic use may decrease power and accuracy in pain trials.
      • This study examined several methods to account for analgesic use.
      • Most currently used methods resulted in decreased power and accuracy.
      • A novel outcome that accounts for analgesic use optimized power and minimized bias.

      Abstract

      The 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.

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