On the relationship between pain variability and relief in randomized clinical trials

Previous research suggests greater baseline variability is associated with greater pain relief in those who receive a placebo. However, studies that evidence this association do not control for confounding effects (natural history and regression-to-the-mean); for this reason, we analyzed data from two randomized clinical trials (Placebo I and Placebo II, N = 134) while adjusting for confounding effects via a no-treatment group. Results agree between the two placebo groups: both placebo groups showed a negligible correlation between baseline variability and adjusted response (r sp (CI 95% ) = 0.13 (−0.09, 0.37) and 0.01 (−0.15, 0.20) for Placebo I and II, respectively). Drug groups also showed similar, weak correlations (rsp = −0.16–0.08; max CI 95% = −0.39–0.31). When modeled as a linear covariate, variability only accounted for an additional 1% of the variance in post-intervention pain across both studies; the inability of variability to account for substantial variance in pain response highlights that previous results concerning variability and treatment response may be inconsistent. Indeed, the relationship appears to be neither consistently specific nor sensitive to improvements in the placebo group. Researchers and clinicians should not rely on using baseline pain variability as a prognostic factor for improvement following placebo

EutroPod

EutroPod is a highly concentrated solution of denitrifying enzymes used to combat eutrophication. This product alters the chemical structure of nitrates and nitrites by converting them into a form that is safe for the environment

What is the numerical nature of pain relief?

Pain relief, or a decrease in self-reported pain intensity, is frequently the primary outcome of pain 10 clinical trials. Investigators commonly report pain relief in one of two ways: using raw units (additive) 11 or using percentage units (multiplicative). However, additive and multiplicative scales have different 12 assumptions and are incompatible with one-another. In this work, we describe the assumptions and 13 corollaries of additive and multiplicative models of pain relief to illuminate the issue from statistical 14 and clinical perspectives. First, we explain the math underlying each model and illustrate these points 15 using simulations, for which readers are assumed to have an understanding of linear regression. Next, we 16 connect this math to clinical interpretations, stressing the importance of statistical models that accurately 17 represent the underlying data; for example, how using percent pain relief can mislead clinicians if the data are actually additive. These theoretical discussions are supported by empirical data from four 19 longitudinal studies of patients with subacute and chronic pain. Finally, we discuss self-reported pain 20 intensity as a measurement construct, including its philosophical limitations and how clinical pain differs 21 from acute pain measured during psychophysics experiments. This work has broad implications for 22 clinical pain research, ranging from statistical modeling of trial data to the use of minimal clinically important differences and patient-clinician communication

Chronic Bronchitis

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The Brain Family

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Is “Percent Pain Reduction” a Valid Metric of Clinical Pain Improvement?

Chronic pain is the most prevalent health condition in the United States, affecting over 116 million Americans, and therefore is the focus of many clinical trials. Studies on chronic pain treatment commonly report improvements in pain as a percent reduction from an initial value. Importantly percent reduction implies that improvements in pain are multiplicative. Although percent reductions are conceptually simple, empirically, it is unclear whether changes are truly multiplicative in nature. We assessed the validity of this assumption using longitudinal data from multiple randomized controlled trials. In each dataset, we assessed the presence of two hallmarks of a multiplicative effect: (1) whether the decrease in pain scales with initial pain; and (2) whether the residual error scales with greater pain ratings. The data did not meet either of these conditions. Since (1) changes in pain did not correlate with pre-intervention pain ratings and (2) residual error did not scale with post-intervention pain, pain reductions do not exhibit multiplicative properties. Instead, the data appear additive rather than multiplicative. Thus, reporting percent reductions in pain may be misleading. Instead, researchers and clinicians should report differences in pain, which more appropriately, represents the nature of changes in clinical pain

EutroPod

EutroPod is a highly concentrated solution of denitrifying enzymes used to combat eutrophication. This product alters the chemical structure of nitrates and nitrites by converting them into a form that is safe for the environment.https://digitalcommons.imsa.edu/cii_dsw/1001/thumbnail.jp

On the relationship between pain variability and relief in randomized clinical trials

Previous research reports suggest greater baseline variability is associated with greater pain relief in those who receive a placebo. However, studies that evidence this association do not control for confounding effects from regression to the mean and natural history. In this report, we analyzed data from two randomized clinical trials (Placebo I and Placebo II, total N = 139) while adjusting for the effects of natural history and regression to the mean via a no treatment group. Results agree between the two placebo groups in each study: both placebo groups showed negligible semi-partial correlations between baseline variability and adjusted response [rsp (CI95%) = 0.22 (0.03, 0.42) and 0 (−0.07, 0.07) for Placebo I and II, respectively]. The no-treatment group in Placebo I showed a negative correlation [−0.22(−0.43,−0.02)], but the no-treatment and drug groups in Placebo II’s correlations were negligible [−0.02(−0.08,0.02) and 0.00 (−0.10, 0.12) for the no-treatment and drug groups, respectively]. When modeled as a linear covariate, baseline pain variability accounted for less than 1% of the variance in post-intervention pain across both studies. Even after adjusting for baseline pain and natural history, the inability of baseline pain variability to account for substantial variance in pain response highlights that previous results concerning pain variability and treatment response may be inconsistent. Indeed, the relationship appears to be neither consistently specific nor sensitive to improvements in the placebo group. More work is needed to understand and establish the prognostic value of baseline pain variability—especially its placebo specificity and generalizability across patient populations