One of the primary purposes of creating an item bank for adaptive testing is to measure patient health status in a precise and efficient manner. Several decisions are required during the construction of an item bank. One of the most critical is to determine a "real" gap between adjacent items in order to assess the necessity of adding new items in that area.
A commonly used criterion is that a meaningful, i.e., substantively significant, gap is a calibration difference of 0.5 logit or more. While useful psychometrically, this standard may overlook important differences that arise from differences in health status or even disease type. We offer an alternative to the psychometric standard of the 0.5 logit difference, namely clinically important or meaningful differences (CMD). We propose that a gap should be defined as "the calibration difference of two adjacent items equal to or greater than a CMD". In contrast, the gap will be considered negligible if the calibration difference between two adjacent items is less than the value of the CMD. That is, there is no clinical motivation to fill gaps less than the value of the CMD.
|Raw Score Range on FACT-Fatigue||Clinically Meaningful Distance (CMD) in Logits|
The sizes of clinically meaningful gaps vary along the measurement continuum (Hays & Woolley, 2000). As a first step, Cella et al. estimated CMD sizes by triangulating anchor- and distribution-based approaches. They determined that 3 raw score units on the Fatigue subscale of the Functional Assessment of Cancer Therapy (FACT-Fatigue) provide reasonable initial estimates of CMD size all along the continuum. After conversion of the raw score CMD values to logits, we arrived at 4 gap values based upon the location along the fatigue continuum (see Table). These gap sizes provide guidance as to where further items are needed, and will ultimately enhance the clinical relevance of item banking and adaptive testing in health status and quality of life measurement. Better techniques for estimating CMD size are under development.
Jin-shei Lai and David T. Eton.
Center on Outcomes Research and Education (CORE), Evanston Northwestern Healthcare and Northwestern University
Cella, D., Eton, D.T., Lai, J-S., & Peterman, A.H. (in press). Combining anchor and distribution based methods to derive clinically meaningful differences on the Functional Assessment of Cancer Therapy (FACT) anemia and fatigue scales. Journal of Pain & Symptom Management.
Hays, R.D. & Woolley, J.M. (2000). The concept of clinically meaningful difference in health-related quality-of-life research: How meaningful is it? Pharmacoeconomics, 18, pp. 419-423.
Clinically Meaningful Gaps Lai J.-S., Eton D.T. Rasch Measurement Transactions, 2002, 15:4 p. 850
|Rasch Measurement Transactions (free, online)||Rasch Measurement research papers (free, online)||Probabilistic Models for Some Intelligence and Attainment Tests, Georg Rasch||Applying the Rasch Model 3rd. Ed., Bond & Fox||Best Test Design, Wright & Stone|
|Rating Scale Analysis, Wright & Masters||Introduction to Rasch Measurement, E. Smith & R. Smith||Introduction to Many-Facet Rasch Measurement, Thomas Eckes||Invariant Measurement: Using Rasch Models in the Social, Behavioral, and Health Sciences, George Engelhard, Jr.||Statistical Analyses for Language Testers, Rita Green|
|Rasch Models: Foundations, Recent Developments, and Applications, Fischer & Molenaar||Journal of Applied Measurement||Rasch models for measurement, David Andrich||Constructing Measures, Mark Wilson||Rasch Analysis in the Human Sciences, Boone, Stave, Yale|
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