TY - JOUR
T1 - What Is the Numerical Nature of Pain Relief?
AU - Vigotsky, Andrew D.
AU - Tiwari, Siddharth R.
AU - Griffith, James W.
AU - Apkarian, A. Vania
N1 - Funding Information:
This work was funded by the National Institutes of Health (1P50DA044121-01A1). This material was based upon work supported by the National Science Foundation Graduate Research Fellowship under Grant No. DGE-1324585.
Publisher Copyright:
Copyright © 2021 Vigotsky, Tiwari, Griffith and Apkarian.
PY - 2021
Y1 - 2021
N2 - Pain relief, or a decrease in self-reported pain intensity, is frequently the primary outcome of pain clinical trials. Investigators commonly report pain relief in one of two ways: using raw units (additive) or using percentage units (multiplicative). However, additive and multiplicative scales have different assumptions and are incompatible with one another. In this work, we describe the assumptions and corollaries of additive and multiplicative models of pain relief to illuminate the issue from statistical and clinical perspectives. First, we explain the math underlying each model and illustrate these points using simulations, for which readers are assumed to have an understanding of linear regression. Next, we connect this math to clinical interpretations, stressing the importance of statistical models that accurately 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 longitudinal studies of patients with subacute and chronic pain. Finally, we discuss self-reported pain intensity as a measurement construct, including its philosophical limitations and how clinical pain differs from acute pain measured during psychophysics experiments. This work has broad implications for clinical pain research, ranging from statistical modeling of trial data to the use of minimal clinically important differences and patient-clinician communication.
AB - Pain relief, or a decrease in self-reported pain intensity, is frequently the primary outcome of pain clinical trials. Investigators commonly report pain relief in one of two ways: using raw units (additive) or using percentage units (multiplicative). However, additive and multiplicative scales have different assumptions and are incompatible with one another. In this work, we describe the assumptions and corollaries of additive and multiplicative models of pain relief to illuminate the issue from statistical and clinical perspectives. First, we explain the math underlying each model and illustrate these points using simulations, for which readers are assumed to have an understanding of linear regression. Next, we connect this math to clinical interpretations, stressing the importance of statistical models that accurately 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 longitudinal studies of patients with subacute and chronic pain. Finally, we discuss self-reported pain intensity as a measurement construct, including its philosophical limitations and how clinical pain differs from acute pain measured during psychophysics experiments. This work has broad implications for clinical pain research, ranging from statistical modeling of trial data to the use of minimal clinically important differences and patient-clinician communication.
KW - ANCOVA
KW - clinical trials
KW - pain
KW - statistical models
KW - treatment effects
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U2 - 10.3389/fpain.2021.756680
DO - 10.3389/fpain.2021.756680
M3 - Article
C2 - 35295426
AN - SCOPUS:85133459639
SN - 2673-561X
VL - 2
JO - Frontiers in Pain Research
JF - Frontiers in Pain Research
M1 - 756680
ER -