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.
Department of Biostatistics, University of Washington, Seattle, Washington.
J Pain. 2023 Feb;24(2):332-344. doi: 10.1016/j.jpain.2022.09.017. Epub 2022 Oct 8.
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 QPAC) optimized power and minimized bias. Analyses of real RCT data supported the results of the simulations, showing greater power with analysis of the QPAC 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 QPAC, and the results of statistical simulations and analyses of trial data supporting improvements in power and bias using the QPAC. Methods of this type should be considered in the analysis of pain RCTs.
0 到 10 数字疼痛强度评分是疼痛治疗随机对照试验（RCT）的标准结局。对于正在服用镇痛药的个体，同时使用镇痛药时的“观察到”疼痛强度（同时使用镇痛药时的疼痛强度）和不同时使用镇痛药时的疼痛强度（如果未服用镇痛药，数字评分量表的数值）之间可能存在差异。我们使用当代因果推理框架，首先在统计模拟中，然后在腰椎硬膜外类固醇注射 RCT 的真实（非模拟）数据分析中，比较了可能解释同时使用镇痛药的分析方法。默认的分析方法是忽略镇痛药的使用，这是疼痛 RCT 中最常见的方法。与忽略镇痛药的使用和其他分析方法相比，模拟结果表明，对于正在服用镇痛药的个体，将疼痛强度增加 1.5 分的定量疼痛和镇痛综合结局（QPAC）优化了功效并最小化了偏差。对真实 RCT 数据的分析支持了模拟结果，与忽略镇痛药的使用和大多数其他检查方法相比，QPAC 分析显示出更高的功效。我们提出了在疼痛 RCT 分析中应考虑的替代方法。观点：本文介绍了一种新的定量疼痛和镇痛综合结局 QPAC 的概念框架，以及支持使用 QPAC 提高功效和减少偏差的统计模拟和试验数据分析结果。这类方法应在疼痛 RCT 的分析中加以考虑。
