The Rasch measurement model in rheumatology: What is it and why use it? When should it be applied, and what should one look for in a Rasch paper?

Tennant, Alan; Conaghan, Philip G.

doi:10.1002/art.23108

Cited by 1,303 publications

(1,874 citation statements)

References 23 publications

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“…The PSI was low and met only the minimum required value for group use when rounded to 1 decimal place 12. Item 4 (pain comes on “suddenly and in bursts”) had a high positive fit residual of 3.00.…”

Section: Resultsmentioning

confidence: 96%

“…Several reviews 12, 24, 25 of Rasch analysis have detailed the process and defined terminology; therefore, only a brief overview is provided here. Analysis was conducted in RUMM2020 26 using the dichotomous and partial credit models 27.…”

Section: Methodsmentioning

confidence: 99%

“…This can be achieved with Rasch analysis, which has several statistical advantages, including (given fit to the model) transformation of raw scores into interval‐level data, thereby facilitating, for example, valid analysis of changes in data following an intervention 12. An earlier version of LANSS was not shown to fit the Rasch model in patients with OA 13, but Rasch analyses of S‐LANSS and painDETECT have not been reported.…”

Section: Introductionmentioning

confidence: 99%

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Pain Phenotype in Patients With Knee Osteoarthritis: Classification and Measurement Properties of painDETECT and Self‐Report Leeds Assessment of Neuropathic Symptoms and Signs Scale in a Cross‐Sectional Study

Moreton

Tew

Nair

et al. 2015

Arthritis Care & Research

108

102

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ObjectiveMultiple mechanisms are involved in pain associated with osteoarthritis (OA). The painDETECT and Self‐Report Leeds Assessment of Neuropathic Symptoms and Signs (S‐LANSS) questionnaires screen for neuropathic pain and may also identify individuals with musculoskeletal pain who exhibit abnormal central pain processing. The aim of this cross‐sectional study was to evaluate painDETECT and S‐LANSS for classification agreement and fit to the Rasch model, and to explore their relationship to pain severity and pain mechanisms in OA.MethodsA total of 192 patients with knee OA completed questionnaires covering different aspects of pain. Another group of 77 patients with knee OA completed questionnaires and underwent quantitative sensory testing for pressure–pain thresholds (PPTs). Agreement between painDETECT and S‐LANSS was evaluated using kappa coefficients and receiver operator characteristic (ROC) curves. Rasch analysis of both questionnaires was conducted. Relationships between screening questionnaires and measures of pain severity or PPTs were calculated using correlations.ResultsPainDETECT and S‐LANSS shared a stronger correlation with each other than with measures of pain severity. ROC curves identified optimal cutoff scores for painDETECT and S‐LANSS to maximize agreement, but the kappa coefficient was low (κ = 0.33–0.46). Rasch analysis supported the measurement properties of painDETECT but not those of S‐LANSS. Higher painDETECT scores were associated with widespread reductions in PPTs.ConclusionThe data suggest that painDETECT assesses pain quality associated with augmented central pain processing in patients with OA. Although developed as a screening questionnaire, painDETECT may also function as a measure of characteristics that indicate augmented central pain processing. Agreement between painDETECT and S‐LANSS for pain classification was low, and it is currently unknown which tool may best predict treatment outcome.

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Section: Resultsmentioning

confidence: 96%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Pain Phenotype in Patients With Knee Osteoarthritis: Classification and Measurement Properties of painDETECT and Self‐Report Leeds Assessment of Neuropathic Symptoms and Signs Scale in a Cross‐Sectional Study

Moreton

Tew

Nair

et al. 2015

Arthritis Care & Research

108

102

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“…Proper fit to the Rasch model and transformation to interval-level scaling require a number of criteria to be satisfied: 1) accordance of data structure to the probabilistic form of Guttman scaling (39), 2) unidimensionality (40), and 3) local independence of items. Statistical criteria to be used to evaluate the fit to the Rasch model have been extensively described in a previous review (41), and are only described briefly here.…”

Section: Methods 3: 1-parameter Irt Approach (Rasch Analysis)mentioning

confidence: 99%

Disease‐related differential item functioning in the work instability scale for rheumatoid arthritis: Converging results from three methods

Tang

2011

Arthritis Care & Research

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Objective. The 23-item Work Instability Scale for Rheumatoid Arthritis (RA-WIS) is a promising measure to assess risk for future work disability. Validated in both rheumatoid arthritis (RA) and osteoarthritis (OA), it has high potential for cross-disease applications. Our objective was to examine disease-related differential item functioning (DIF) in the RA-WIS. Methods. Workers with RA (n ‫؍‬ 120) or OA (n ‫؍‬ 130) were recruited from 3 sites and completed a questionnaire consisting of demographic and health-and work-related variables, including the RA-WIS (range 0 -23, where 23 ‫؍‬ highest work instability). Multiple DIF detection methods were applied for comparability: 1) Mantel-Haenszel and Breslow-Day procedures, 2) hierarchical 3-step sequential logistic regression procedure, and 3) a 1-parameter item response theory approach (Rasch analysis). Both tests of significance (chi-square and F tests) and effect size statistics (⌬ MH , ⌬R 2 ) were assessed to confirm items demonstrating uniform or nonuniform DIF. A 2-step purification procedure was applied to establish a DIF-free conditioning variable (total RA-WIS score) for DIF analyses. The resultant impact of disease-related DIF at the scale level was also evaluated. Results. All 3 DIF detection methods converged to reveal 3 RA-WIS items as having significant disease-related uniform DIF. Two items ("difficulty opening doors" and "pressure on hand") were more likely affirmed in RA, while 1 item ("very stiff") was more likely affirmed in OA. Overall, only a marginal impact at the scale level was found due to a small proportion of scale items exhibiting DIF and the bidirectional nature of DIF effects. Conclusion. RA-WIS scores can be directly compared between RA and OA without significant concerns for DIF-related measurement bias.

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“…The probability that a respondent will give a particular response to a particular question is calculated as a logistic function of the distance between the position of the respondent and the position of that response level on the latent (logit) scale. Rasch models can be used to analyse the psychometric properties of existing scales and develop new scales [92; 93] and offer a number of advantages compared to traditional psychometric techniques 1 [94].…”

Section: Overview Of the Literaturementioning

confidence: 99%

A Systematic Review of the Literature on the Development of Condition-Specific Preference-Based Measures of Health

Goodwin

Green

2016

Appl Health Econ Health Policy

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Background. Health state utility values (HSUVs) are required to calculate quality-adjusted life-years (QALYs). They are frequently derived from generic preference-based measures of health. However, such generic measures may not capture health attributes of relevance to specific conditions. In such cases, a condition-specific preference-based measure (CSPBM) may be more appropriate.Objective. This systematic review aimed to identify all published accounts of developing CSPBMs, to describe and appraise the methods used. Results. Eighty-six publications were identified, describing 51 CSPBMs. Around two-thirds of these were QALY measures; the remainder were designed for clinical decision-making only. Classification systems for 33 CSPBMs were derived from existing instruments; 18 were developed de novo. HSUVs for 34 instruments were estimated using a 'composite' approach, involving statistical modelling; the remainder used a 'decomposed' approach based on multi-attribute utility theory. Half of the papers that described the estimation of HSUVs did not report validating their measures.Conclusion. Various methods have been used at all stages of CSPBM development. The choice between developing a classification system de novo or from an existing instrument may depend on the availability of a suitable existing measure, while the choice between a decomposed or composite approach appears to be determined primarily by the purpose for which the instrument is designed. The validation of CSPBMs remains an area for further development. 3 Key Points for Decision MakersThere are two main approaches to developing a classification system (derivation from an existing instrument or de novo) and two main approaches to estimating health state utility values (using statistical regression or multi-attribute utility theory) for a condition-specific preference based measure.There is more evidence available to support the validity of classification systems that were derived from existing measures of health-related quality of life than there is for classification systems developed de novo.

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The Rasch measurement model in rheumatology: What is it and why use it? When should it be applied, and what should one look for in a Rasch paper?

Cited by 1,303 publications

References 23 publications

Pain Phenotype in Patients With Knee Osteoarthritis: Classification and Measurement Properties of painDETECT and Self‐Report Leeds Assessment of Neuropathic Symptoms and Signs Scale in a Cross‐Sectional Study

Pain Phenotype in Patients With Knee Osteoarthritis: Classification and Measurement Properties of painDETECT and Self‐Report Leeds Assessment of Neuropathic Symptoms and Signs Scale in a Cross‐Sectional Study

Disease‐related differential item functioning in the work instability scale for rheumatoid arthritis: Converging results from three methods

A Systematic Review of the Literature on the Development of Condition-Specific Preference-Based Measures of Health

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