Journal article
Statistical modeling of acute and chronic pain patient-reported outcomes obtained from ecological momentary assessment
Pain (Amsterdam), Vol.165(9), pp.1955-1965
05/07/2024
DOI: 10.1097/j.pain.0000000000003214
PMCID: PMC11813191
PMID: 38718196
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.
Details
- Title: Subtitle
- Statistical modeling of acute and chronic pain patient-reported outcomes obtained from ecological momentary assessment
- Creators
- Andrew Leroux - University of Colorado Anschutz Medical CampusCiprian Crainiceanu - Johns Hopkins UniversityScott Zeger - Johns Hopkins UniversityMargaret Taub - Johns Hopkins UniversityBriha Ansari - Johns Hopkins UniversityTor D Wager - Dartmouth CollegeEmine Bayman - University of IowaChristopher Coffey - Cancer Research And BiostatisticsCarl Langefeld - Wake Forest UniversityRobert McCarthy - Rush UniversityAlex Tsodikov - University of MichiganChad Brummet - University of MichiganDaniel J Clauw - University of MichiganRobert R Edwards - Harvard UniversityMartin A Lindquist - Johns Hopkins UniversityAcute to Chronic Pain Signatures (A2CPS) Consortium
- Resource Type
- Journal article
- Publication Details
- Pain (Amsterdam), Vol.165(9), pp.1955-1965
- DOI
- 10.1097/j.pain.0000000000003214
- PMID
- 38718196
- PMCID
- PMC11813191
- NLM abbreviation
- Pain
- ISSN
- 0304-3959
- eISSN
- 1872-6623
- Publisher
- LIPPINCOTT WILLIAMS & WILKINS
- Grant note
- National Institutes of Health Common Fund: U24NS112873, U54DA049110, U54DA049116, U54DA049115, U54DA049113, UM1NS112874, UM1NS118922
The A2CPS Consortium is supported by the National Institutes of Health Common Fund, which is managed by the OD/Office of Strategic Coordination (OSC). Consortium components include Clinical Coordinating Center (U24NS112873), Data Integration and Resource Center (U54DA049110), Omics Data Generation Centers (U54DA049116, U54DA049115, U54DA049113), MCC 1 (UM1NS112874), and MCC 2 (UM1NS118922). The authors also acknowledge the University of Iowa for coordination and implementation of the single IRB model, the External Program Consultants, and the Patient Consultants for their contributions to the design and implementation of the projects.
- Language
- English
- Electronic publication date
- 05/07/2024
- Academic Unit
- Biostatistics; Anesthesia
- Record Identifier
- 9984627150202771
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