Abstract
Objective. The Rasch measurement model provides robust analysis of the internal construct validity of outcome measures. We reviewed the application of Rasch analysis in musculoskeletal medicine as part of the work leading to discussion in a Special Interest Group in Rasch Analysis at Outcome Measures in Rheumatology 11.
Methods. A systematic literature review of SCOPUS and MEDLINE was performed (January 1, 1985, to February 29, 2012. Original research reports in English using “Rasch” or “Item Response Theory” in musculoskeletal diseases were assessed by 2 independent reviewers. The topics of focus and analysis methodology details were recorded.
Results. Of 212 articles reviewed, 114 were included. The number of publications rose from 1 in 1991–1992 to 23 in 2011–February 2012. Disease areas included rheumatoid arthritis (28%), osteoarthritis (16.6%), and general musculoskeletal disorders (43%). Sixty-six reports (57.9%) evaluated psychometric properties of existing scales and 35 (30.7%) involved development of new scales. Nine articles (7.9%) were on methodology illustration. Four articles were on item banking and computer adaptive testing. A majority of the articles reported fit statistics, while the basic Rasch model assumption (i.e., unidimensionality) was examined in only 57.2% of the articles. An improvement in reporting qualities with Rasch articles was noted over time. In addition, only 11.4% of the articles provided a transformation table for interval scale measurement in clinical practice.
Conclusion. The Rasch model has been increasingly used in rheumatology over the last 2 decades in a wide range of applications. The majority of the articles demonstrated reasonable quality of reporting. Improvements in quality of reporting over time were revealed.
The Rasch measurement model provides a robust analysis of the internal construct validity of outcome measures1. Fitting data to the model, a process known as Rasch analysis, is increasingly used in health sciences and in rheumatology, with its application ranging from evaluation of the psychometric properties of existing patient-reported outcome measures, revision of observer-evaluated scales like imaging scores, and development of new instruments, to the concept of item banking and computer adaptive testing schedule (CAT)2. The process, which involves several components, is iterative and the recommendations for the essential components to be assessed and reported for Rasch analysis have been published2.
Our aim was to review the application and quality of reporting of Rasch analysis in the field of rheumatology and musculoskeletal (MSK) disorders over the past 2 decades. This provides data on the acceptance of using the Rasch model in research and clinical practice, and reveals the comprehensiveness and quality of reporting of Rasch statistics over time. At the recent Outcome Measures in Rheumatology 11 (OMERACT) meeting in Pinehurst, North Carolina, USA, a Rasch Special Interest Group (SIG) was established to examine the application of the model across rheumatic diseases. This article reports the work leading to the Rasch SIG in OMERACT 11.
MATERIALS AND METHODS
Search Strategies
A systematic review of SCOPUS and MEDLINE database from January 1, 1985, to February 29, 2012, was performed to identify English-language original research reports with keywords “Rasch” or “Item Response Theory” and “rheumatology” or “arthritis” or “back pain” or “neck pain” or “musculoskeletal.” The references of all retrieved articles were also screened for potentially relevant publications.
Selection of Articles
Two reviewers (LYY and PME) independently assessed inclusion of articles, and disputes were resolved by a third reviewer (AT). We included publications that used Rasch model analysis in the evaluation of any instrument in people with MSK conditions. We excluded reports from general rehabilitation medicine and populations that included a variety of diagnoses, unless they evaluated the performance of instruments in the MSK disorders group separately or their primary aim was to compare the differential item functioning (DIF) between 2 groups, of which 1 was a defined MSK group.
Quality Assessment
Two reviewers (LYY, PME) independently scored the methodological quality of each article using a standardized checklist as described below. The checklist of quality identifiers (QI) was based on the recommendation developed to address the lack of understanding of the analytical technique of the Rasch model and its applications; and to provide guidelines on the essential points to be reported in Rasch analysis2. Only the presence or absence of QI was recorded in our study. Reports concerning polytomous instruments were evaluated on 10 QI, while articles on dichotomous instruments were evaluated with 9 QI. Disagreements between reviewers were identified and discussed during consensus meetings. The checklists of QI were:
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Stating the software for Rasch model analysis. Most Rasch analyses were undertaken with proprietary software including WINSTEPS, RUMM, ConQuest, and others3. Each reports the findings in a slightly different way.
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Reporting the mathematical derivation of the Rasch model. When items in a scale have 2 levels of response, the dichotomous model is chosen. When items have 3 or more levels of response, the Rasch model has at least 2 forms employing slightly different mathematics, namely the Andrich Rating Scale (AS) model or the Masters Partial Credit model2. The main difference is the way they handle the distance between thresholds (the probabilistic midpoint between 2 adjacent categories). The AS model expects the distance between thresholds to be equal across items. The rationale behind choice of model should be reported. In RUMM, there is a likelihood ratio statistic that helps in choosing the best polytomous model.
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Evaluation of the threshold ordering (for polytomous items only). For polytomous items, it is important to assess their category structure and ensure the responses to items are consistent with the metric estimate of the underlying construct. That is, the transition from one category to the next category should reflect and increase with the underlying latent trait being measured. When this does not happen, disordered thresholds are said to occur, and collapsing categories may be necessary. In this analysis, we assessed only whether the report assessed threshold disordering.
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Tests of item fit to the Rasch model. In WINSTEPS, fit is reported as the INFIT and OUTFIT statistics; there is also a standardized fit statistics reported as ZSTD. In RUMM, chi-square statistics measure fit; they also have a residual statistic, which is the standardized sum of all difference between observed and expected values summed over all persons (very similar to the WINSTEPS OUTFIT ZSTD statistic). Although the fit statistics are not directly comparable across software packages, they take the standardized value across all persons and consequently can be similar.
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Tests of person fit. A few respondents with atypical response patterns may seriously affect fit at the item level. Such response patterns would be identified by its high positive residuals. This could be related to unrecorded comorbidities, such as cognitive deficits, and removing these respondents who misfit in this way may significantly improve the internal construct validity of a scale. Thus person fit is important and some form of summary of person fit should be reported.
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Testing for DIF. Previously known as item bias, DIF can affect fit to the model. This occurs when different demographic groups within the sample (e.g., younger and older, male and female) respond in a different manner to an item, despite equal levels of the underlying latent trait being measured. There may be many such different groups in each data set, but at the very least, DIF with age and sex should be assessed.
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Reliability. Both WINSTEPS and RUMM have a reliability measure. In WINSTEPS, an item separation ratio is reported, and the values for group use and individual use are 1.5 and 2.5, respectively. In RUMM, an estimate of the internal consistency reliability is reported as a person separation index (PSI). Prior to version RUMM 2030, this was equivalent to Cronbach’s alpha, which is expressed in the raw form; PSI is in the logit form (linear person estimate). A minimum value of 0.7 and 0.85 is required for group use and individual use, respectively. More recently, the PSI has been made sensitive to the distribution, and will diverge from alpha when the distribution of persons is skewed.
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Response dependency. Response dependency is where items are linked in some way, such that the response on one item will determine the response on another. An example is where several walking items are included in the same scale. A person capable of walking 1 mile without difficulty must be able to walk 0.5 miles without difficulty. If both items (reflecting the different distances) were included in the scale, reliability would be inflated, and the measurement estimation in the Rasch model might also be affected. Dependent items can be identified through the residual correlation matrix, which should show no significant associations.
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Unidimensionality. The Rasch model is a unidimensional measurement model, with the assumption that the items summed together form a unidimensional scale. There are various ways to test this. Rasch software packages usually provide a principal component analysis of the residuals. In WINSTEPS, the magnitude of the first contrast of the residual is an important indicator and generally should not be above 2. In RUMM, a series of independent t tests is conducted with the pairs of person measures fitted from 2 subsets of items identified to load positively and negatively on the first component of the residuals. Person estimates derived from the positive set of items are contrasted against those derived from the negative set. A series of individual t tests is undertaken to compare the estimates for each person. The percentage of these tests outside the range −1.96 to 1.96 should not exceed 5%. Provided the differences in estimates derived from the 2 subsets of items are normally distributed, this approach is robust enough to detect strict unidimensionality4.
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Transformation table. When an instrument fulfills the Rasch model, it can be transformed to interval scale for measurement, and a transformation table should be made available for this conversion. Equal interval scaling is important in clinical trials for accurately informing the magnitude of change and the reporting of responsiveness measures (such as minimal important differences and effect size).
RESULTS
Search Results
The search strategy resulted in 200 articles, together with 12 additional articles that were identified from the references of retrieved articles. We included a total of 114 articles in this review; the reasons for exclusion are given in Table 1. The details of included articles are summarized in Supplementary Table 1 available online at jrheum.org. Disease areas included rheumatoid arthritis (28%), osteoarthritis (OA, 16.6%), and general MSK disorders (43%). For the application of Rasch analysis, 66 (57.9%) reviewed the psychometric properties of existing instruments in ordinal scales, including cross-cultural adaptation of existing instruments. Thirty-five articles (30.7%) described the development of new instruments using the Rasch model. Among these articles, 6 described using Rasch analysis to shorten existing instruments to reduce respondent burden5,6,7,8,9,10. Nine articles (7.9%) aimed to teach or illustrate a methodology, usually with data originally collected for another purpose. Only a minority (4 articles) reported on the construction of an item bank or application of CAT11,12,13,14. The number of publications using Rasch analysis, considered in 2-year blocks, rises from 1 in 1991–1992 to 21 in 2009–2010 and 23 in the subsequent 14 months (January 1, 2011–February 29, 2012; Figure 1).
Number of Rasch articles, in 2-year blocks.
Excluded articles and reason for exclusion (n = 98).
For the 5 most frequently cited articles6,10,15,16,17, their average annual citation rates ranged from 9.78 to 18.17. The reason for high citation in 3 of these articles was the evaluation of generic instruments in the rehabilitation setting, where the instruments have wide application in a wide range of patient groups6,10,17. Doward, et al reported the development of a specific and patient-derived quality-of-life instrument for ankylosing spondylitis, the first of its kind in that condition, and thus bridged a significant gap in the literature16. The cross-cultural adaptation of the Dutch version of Western Ontario and McMaster Universities Osteoarthritis Index in hip OA also had a high citation rate, mainly due to the high prevalence of OA and strong Dutch centers in this area of research15.
The Quality of Reporting
Table 2 summarizes the percentage of articles reporting our list of QI. Around 40% of the articles were using WINSTEPS or RUMM for analysis while up to 17% did not report the software they used. There was a shift from use of WINSEPS to RUMM over time. Among the instruments with polytomous item response, nearly half of the articles did not state the mathematical derivative of Rasch model chosen. Among those that reported the model, only a quarter stated the rationale behind choosing a particular model. However, the percentage reporting the Rasch model derivative and the rationale of choice has increased in recent years (Table 2).
Quality identifiers reported for Rasch articles (n = 114).
There were 21 (18.4%) and 91 (79.8%) articles describing instruments with dichotomous and polytomous item response, and 1.8% were mixed. Among the 91 articles examining polytomous scaling and 2 articles with mixed scaling, 53.8% described threshold disordering. A higher percentage of threshold order evaluation was noted in more recent articles.
The majority of the articles reported item fit statistics while person fit statistics were reported in barely one-third. The person reliability indexes were reported in 72 articles (63.2%). Unidimensionality and item independence, which are the basic assumptions of the Rasch model, were examined in 57.2% and 42.11%, respectively. DIF or systemic bias was examined in slightly more than one-half.
Again, better reporting qualities were noted in more recent articles (Table 2). Overall, a higher mean number of QI were reported in more recent articles (Table 3). However, transformation tables for Rasch interval scoring were available only in 11.4%, and remained static with time.
Mean number of quality identifiers (QI) for articles in 5-year blocks.
DISCUSSION
The Rasch model has been widely used over the past 20 years to assess the quality, and help in the development, of patient-reported outcomes in health in general, and in rheumatology. Our review has shown a steady increase in use of Rasch model over time, and a concurrent steady improvement in the quality of reporting. While the most common use was either the evaluation of existing or new scales, more recent applications include the development of item banks, and the application of CAT. The majority of the articles reported item fit, but other QI were reported to a much lesser extent. Unidimensionality, which is the basic assumption of the Rasch model, was reported in only about one-half of the reports. In addition, other important QI like DIF, local independence, and reliability were reported in only about one-half of the articles. However, the field has become more sophisticated over the years, with more recent articles improving in their reporting details.
The dissemination of the use of Rasch model has, in part, been hindered by the different Rasch software packages providing different fit statistics and different ways of assessing assumptions such as unidimensionality. This problem may increase with the advent of new packages introducing yet further fit statistics. The absence of the methodology within the mainstream statistical packages also continues to limit the uptake of the approach, including item response theory (IRT) approaches in general. This is partially reflected when the most commonly cited papers reported in our review have relatively modest average annual citation rates. However, in the context of outcomes, landmark papers such as the one introducing the Health Assessment Questionnaire have on average 70 citations a year18, and the original paper for the Arthritis Impact Measurement Scale averages fewer than 20 citations per year19. Thus, given that the highest-cited Rasch paper in MSK disorders averages nearly 19 citations per year, there can be said to be progress.
Although the Rasch model has traditionally been packaged within the field of IRT, it has unique measurement properties that distinguish it from other IRT models, and lately strong arguments have been put forward that suggest the 2 approaches — Rasch Measurement and IRT — are incompatible20. Here, the former is seen within the framework of experimental measurement, and the latter within the framework of statistical modeling. Recently, mathematical proofs published showed that the Rasch model defines the theory of simultaneous conjoint measurement in a probabilistic framework, thus satisfying the axioms needed to produce interval scaled latent estimates21. Thus when a data set meets the Rasch model expectations, an interval (logit-based) estimate can be derived and this could be used whenever change scores need to be calculated from ordinal scales. Meaningful measurement is based on the arithmetical property of interval scales22,23, and this includes the evaluation of validity and reliability; effect size, responsiveness, and minimally clinically important differences, which are the basis for “truth” and “discrimination” in the OMERACT filter, respectively24. This is one crucial reason for promoting the use of the Rasch model. Unfortunately, as yet, we have shown that there were only a very small number of articles that provided transformation tables to convert ordinal to interval scales for routine use in monitoring outcome and for the calculation of responsiveness and other aspects of change. The reporting of transformation tables has remained static over time, limiting the application of Rasch transformed interval measurement in clinical practice.
Looking to the future, a new direction of development for CAT can be expected in medical outcome measurement that is based on calibrated item banks (where the difficulty of items has been previously established on a single metric)25. It is then possible to use computer algorithms to present items to patients in such a way that their level on the construct to be measured can be determined by just a few questions26. This approach will greatly reduce the item load and respondent burden, with little reduction in the precision of patient ability estimates; and fulfill the “feasibility” of the OMERACT filter24. For example, Elhen, et al11 described the application of a CAT program for assessing disability in patients with mechanical low back pain using factor analysis and Rasch analysis. The initial 108 items were identified from various instruments for disability measurement, and were calibrated onto a single matrix using data collected from 399 patients with mechanical low back pain. An exploratory factor analysis identified 2 domains, namely body function (40 items) and activity participation (54 items). The 2 domains were submitted to the Rasch model separately. The resultant item bank consisted of 33 items in body function and 49 items in activity-participation domains, which fulfilled the Rasch model of unidimensionality, item local independence, showed reliable, adequate item, and person fits, and was free of DIF with age and sex. A CAT program was developed following the logic of Thissen and Mislevy27 and application by software, SmartCAT (v1.0). The CAT application was reevaluated in 133 patients with mechanical low back pain and found to have high correlation with the original 82-item item bank. On average only 19 items in the body function and 14 items in the activity participation were needed to estimate the precise disability levels using the CAT program.
There are several limitations in our review. First, there are many articles in the rehabilitation area that were not included in the current review. Some may have included MSK disorders, although we did include those where such conditions were clearly identified with separate results. Second, both Rasch and item response theory are not terms in the Medical Subject Headings and so we may have missed some articles if these terms do not appear in their title or abstract. Third, we assessed only the presence or absence of a limited list of QI, without examining in detail the quality of statistical analysis. Finally, we did not examine the use of Rasch transformed scales, which may be a good indicator of the application of the Rasch model.
The Rasch model has been gaining increasing acceptance in the field of rheumatology. We have shown that it has been used increasingly with improvement in reporting qualities over the past 2 decades for a wide range of applications. A few articles have demonstrated success in providing Rasch transformed scores for interval measurement, using the Rasch model in item banking and subsequent CAT application. This would be an exciting field for further development in outcome measures to improve truth, discrimination, and most of all feasibility of the OMERACT filter.
ONLINE SUPPLEMENT
Supplementary data for this article are available online at jrheum.org.
