Abstract

BackgroundThe Patient-Rated Wrist Evaluation (PRWE) was developed as a wrist joint specific measure of pain and disability and evidence of sound validity has been accumulated through classical psychometric methods. Rasch analysis (RA) has been endorsed as a newer method for analyzing the clinical measurement properties of self-report outcome measures. The purpose of this study was to evaluate the PRWE using Rasch modeling.MethodsWe employed the Rasch model to assess overall fit, response scaling, individual item fit, differential item functioning (DIF), local dependency, unidimensionality and person separation index (PSI). A convenience sample of 382 patients with distal radius fracture was recruited from the hand and upper limb clinic at large academic healthcare organization, London, Ontario, Canada, 6-month post-injury scores of the PRWE was used. RA was conducted on the 3 subscales (pain, specific activities, and usual activities) of the PRWE separately.ResultsThe pain subscale adequately fit the Rasch model when item 4 “Pain - When it is at its worst” was deleted to eliminate non-uniform DIF by age group, and item 5 “How often do you have pain” was rescored by collapsing into 8 intervals to eliminate disordered thresholds. Uniform DIF for “Use my affected hand to push up from the chair” (by work status) and “Use bathroom tissue with my affected hand” (by injured hand) was addressed by splitting the items for analysis. After background rescoring of 2 items in pain subscale, 2 items in specific activities and 3 items in usual activities, all three subscales of the PRWE were well targeted and had high reliability (PSI = 0.86). These changes provided a unidimensional, interval-level scaled measure.ConclusionLike a previous analysis of the Patient-Rated Wrist and Hand Evaluation, this study found the PRWE could be fit to the Rasch model with rescoring of multiple items. However, the modifications required to achieve fit were not the same across studies, our fit statistics also suggested one of the pain items should be deleted. This study adds to the pool of evidence supporting the PRWE, but cannot confidently provide a Rasch-based scoring algorithm.

Highlights

  • The Patient-Rated Wrist Evaluation (PRWE) was developed as a wrist joint specific measure of pain and disability and evidence of sound validity has been accumulated through classical psychometric methods

  • Rasch uses probabilistic modeling to determine the degree to which items on a scale function as linear measurement of the latent construct, or domains of interest. It models the predicted amounts of this latent construct within the individuals studied [9, 10]. While this interval-level of measurement is a pre-requisite for much statistical analysis, many scales developed within Classical test theory (CTT) fail to meet this interval measurement standard and are used for decision making and statistical purposes, this may influence the validity of research findings [5]

  • Around zero (Can range between ± 2.5); S.D. should be approx. 1; Chi-square value is expected to be small and statistically non-significant; For a measure to be unidimensional per C < 5% should be less than 0.05; if it is higher than 0.05 look into the lower limit the 95% confidence interval if it is less than 0.05 the measure is unidimensional person separation index (PSI) (Person separation index) should be greater than 0.70 for the summary statistics to be reliable; from a chair”) (F = 15.769, df 1, p = 0.000091) and item 6 (“Use bathroom tissue with my affected hand”) (F = 0.183, df 1, p = 0.669405) by both work status and injured hand

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Summary

Introduction

The Patient-Rated Wrist Evaluation (PRWE) was developed as a wrist joint specific measure of pain and disability and evidence of sound validity has been accumulated through classical psychometric methods. Rasch uses probabilistic modeling to determine the degree to which items on a scale function as linear (interval-level) measurement of the latent construct, or domains of interest. It models the predicted amounts of this latent construct within the individuals studied [9, 10]. While this interval-level of measurement is a pre-requisite for much statistical analysis, many scales developed within CTT fail to meet this interval measurement standard and are used for decision making and statistical purposes, this may influence the validity of research findings [5]

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