Application of a Bayesian graded response model to characterize areas of disagreement between clinician and patient grading of symptomatic adverse events Public Deposited

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Creator
  • Reeve, Bryce B
  • Rogak, Lauren J
  • Basch, Ethan
  • Dueck, Amylou C
  • Atkinson, Thomas M
  • Bennett, Antonia V
  • Li, Yuelin
  • Mendoza, Tito R
Abstract
  • Abstract Background Traditional concordance metrics have shortcomings based on dataset characteristics (e.g., multiple attributes rated, missing data); therefore it is necessary to explore supplemental approaches to quantifying agreement between independent assessments. The purpose of this methodological paper is to apply an Item Response Theory (IRT) -based framework to an existing dataset that included unidimensional clinician and multiple attribute patient ratings of symptomatic adverse events (AEs), and explore the utility of this method in patient-reported outcome (PRO) and health-related quality of life (HRQOL) research. Methods Data were derived from a National Cancer Institute-sponsored study examining the validity of a measurement system (PRO-CTCAE) for patient self-reporting of AEs in cancer patients receiving treatment (N = 940). AEs included 13 multiple attribute patient-reported symptoms that had corresponding unidimensional clinician AE grades. A Bayesian IRT Model was fitted to calculate the latent grading thresholds between raters. The posterior mean values of the model-fitted item responses were calculated to represent model-based AE grades obtained from patients and clinicians. Results Model-based AE grades showed a general pattern of clinician underestimation relative to patient-graded AEs. However, the magnitude of clinician underestimation was associated with AE severity, such that clinicians’ underestimation was more pronounced for moderate/very severe model-estimated AEs, and less so with mild AEs. Conclusions The Bayesian IRT approach reconciles multiple symptom attributes and elaborates on the patterns of clinician-patient non-concordance beyond that provided by traditional metrics. This IRT-based technique may be used as a supplemental tool to detect and characterize nuanced differences in patient-, clinician-, and proxy-based ratings of HRQOL and patient-centered outcomes. Trial registration ClinicalTrials.gov NCT01031641 . Registered 1 December 2009.
Date of publication
Identifier
  • doi:10.1186/s41687-018-0086-x
Resource type
  • Article
Rights holder
  • The Author(s)
Language
  • English
Bibliographic citation
  • Journal of Patient-Reported Outcomes. 2018 Dec 04;2(1):56
Publisher
  • Springer International Publishing
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