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Statistical modeling of acute and chronic pain patient-reported outcomes obtained from ecological momentary assessment.
Leroux, Andrew; Crainiceanu, Ciprian; Zeger, Scott; Taub, Margaret; Ansari, Briha; Wager, Tor D; Bayman, Emine; Coffey, Christopher; Langefeld, Carl; McCarthy, Robert; Tsodikov, Alex; Brummet, Chad; Clauw, Daniel J; Edwards, Robert R; Lindquist, Martin A.
Afiliación
  • Leroux A; Department of Biostatistics and Informatics, Anschutz Medical Campus, University of Colorado, Aurora, CO, United States.
  • Crainiceanu C; Department of Biostatistics, Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD, United States.
  • Zeger S; Department of Biostatistics, Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD, United States.
  • Taub M; Department of Biostatistics, Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD, United States.
  • Ansari B; Department of Biostatistics, Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD, United States.
  • Wager TD; Department of Psychological and Brain Science, Dartmouth College, Hanover, NH, United States.
  • Bayman E; Departments of Biostatistics and.
  • Coffey C; Anesthesia, University of Iowa, Iowa City, IA, United States.
  • Langefeld C; Departments of Biostatistics and.
  • McCarthy R; Department of Biostatistics and Data Science, Wake Forest School of Medicine, Winston Salem, NC, United States.
  • Tsodikov A; The Comprehensive Cancer Center of Wake Forest University, Winston Salem, NC, United States.
  • Brummet C; Department of Anesthesiology, Rush University, Chicago, IL, United States.
  • Clauw DJ; Departments of Biostatistics and.
  • Edwards RR; Anesthesiology, University of Michigan, Ann Arbor, MI, United States.
  • Lindquist MA; Anesthesiology, University of Michigan, Ann Arbor, MI, United States.
Pain ; 165(9): 1955-1965, 2024 Sep 01.
Article en En | MEDLINE | ID: mdl-38718196
ABSTRACT
ABSTRACT Ecological momentary assessment (EMA) allows for the collection of participant-reported outcomes (PROs), including pain, in the normal environment at high resolution and with reduced recall bias. Ecological momentary assessment is an important component in studies of pain, providing detailed information about the frequency, intensity, and degree of interference of individuals' pain. However, there is no universally agreed on standard for summarizing pain measures from repeated PRO assessment using EMA into a single, clinically meaningful measure of pain. Here, we quantify the accuracy of summaries (eg, mean and median) of pain outcomes obtained from EMA and the effect of thresholding these summaries to obtain binary clinical end points of chronic pain status (yes/no). Data applications and simulations indicate that binarizing empirical estimators (eg, sample mean, random intercept linear mixed model) can perform well. However, linear mixed-effect modeling estimators that account for the nonlinear relationship between average and variability of pain scores perform better for quantifying the true average pain and reduce estimation error by up to 50%, with larger improvements for individuals with more variable pain scores. We also show that binarizing pain scores (eg, <3 and ≥3) can lead to a substantial loss of statistical power (40%-50%). Thus, when examining pain outcomes using EMA, the use of linear mixed models using the entire scale (0-10) is superior to splitting the outcomes into 2 groups (<3 and ≥3) providing greater statistical power and sensitivity.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Dimensión del Dolor / Modelos Estadísticos / Dolor Agudo / Dolor Crónico / Medición de Resultados Informados por el Paciente / Evaluación Ecológica Momentánea Límite: Female / Humans / Male Idioma: En Revista: Pain Año: 2024 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Estados Unidos
Texto completo
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Dimensión del Dolor / Modelos Estadísticos / Dolor Agudo / Dolor Crónico / Medición de Resultados Informados por el Paciente / Evaluación Ecológica Momentánea Límite: Female / Humans / Male Idioma: En Revista: Pain Año: 2024 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Estados Unidos