BACKGROUND: In many research and clinical settings in which patient-reported outcome (PRO) measures are used, it is often desirable to link scores across disparate measures or to use scores from 1 measure to describe scores on a separate measure. However, PRO measures are scored by using a variety of metrics, making such comparisons difficult. OBJECTIVE: The objective of this article was to provide an example of how to transform scores across disparate measures (the Marks Asthma Quality of Life Questionnaire [AQLQ-Marks] and the newly developed RAND-Negative Impact of Asthma on Quality of Life item bank [RAND-IAQL-Bank]) by using an item response theory (IRT)-based linking method. METHODS: Our sample of adults with asthma (N = 2032) completed 2 measures of asthma-specific quality of life: the AQLQ-Marks and the RAND-IAQL-Bank. We use IRT-based co-calibration of the 2 measures to provide a linkage, or a common metric, between the 2 measures. Co-calibration refers to the process of using IRT to estimate item parameters that describe the responses to the scales' items according to a common metric; in this case, a normal distribution transformed to a T scale with a mean of 50 and an SD of 10. RESULTS: Respondents had an average age of 43 (15), were 60% female, and predominantly non-Hispanic White (56%), with 19% African American, 14% Hispanic, and 11% Asian. Most had at least some college education (83%), and 90% had experienced an asthma attack during the last 12 months. Our results indicate that the AQLQ-Marks and RAND-IAQL-Bank scales measured highly similar constructs and were sufficiently unidimensional for IRT co-calibration. Once linked, scores from the 2 measures were invariant across subgroups. A crosswalk is provided that allows researchers and clinicians using AQLQ-Marks to crosswalk to the RAND-IAQL toolkit. CONCLUSIONS: The ability to translate scores from the RAND-IAQL toolkit to other "legacy" (ie, commonly used) measures increases the value of the new toolkit, aids in interpretation, and will hopefully facilitate adoption by asthma researchers and clinicians. More generally, the techniques we illustrate can be applied to other newly developed or existing measures in the PRO research field to obtain crosswalks with widely used traditional legacy instruments.
BACKGROUND: In many research and clinical settings in which patient-reported outcome (PRO) measures are used, it is often desirable to link scores across disparate measures or to use scores from 1 measure to describe scores on a separate measure. However, PRO measures are scored by using a variety of metrics, making such comparisons difficult. OBJECTIVE: The objective of this article was to provide an example of how to transform scores across disparate measures (the Marks Asthma Quality of Life Questionnaire [AQLQ-Marks] and the newly developed RAND-Negative Impact of Asthma on Quality of Life item bank [RAND-IAQL-Bank]) by using an item response theory (IRT)-based linking method. METHODS: Our sample of adults with asthma (N = 2032) completed 2 measures of asthma-specific quality of life: the AQLQ-Marks and the RAND-IAQL-Bank. We use IRT-based co-calibration of the 2 measures to provide a linkage, or a common metric, between the 2 measures. Co-calibration refers to the process of using IRT to estimate item parameters that describe the responses to the scales' items according to a common metric; in this case, a normal distribution transformed to a T scale with a mean of 50 and an SD of 10. RESULTS: Respondents had an average age of 43 (15), were 60% female, and predominantly non-Hispanic White (56%), with 19% African American, 14% Hispanic, and 11% Asian. Most had at least some college education (83%), and 90% had experienced an asthma attack during the last 12 months. Our results indicate that the AQLQ-Marks and RAND-IAQL-Bank scales measured highly similar constructs and were sufficiently unidimensional for IRT co-calibration. Once linked, scores from the 2 measures were invariant across subgroups. A crosswalk is provided that allows researchers and clinicians using AQLQ-Marks to crosswalk to the RAND-IAQL toolkit. CONCLUSIONS: The ability to translate scores from the RAND-IAQL toolkit to other "legacy" (ie, commonly used) measures increases the value of the new toolkit, aids in interpretation, and will hopefully facilitate adoption by asthma researchers and clinicians. More generally, the techniques we illustrate can be applied to other newly developed or existing measures in the PRO research field to obtain crosswalks with widely used traditional legacy instruments.
Authors: Sandra R Wilson; Cynthia S Rand; Michael D Cabana; Michael B Foggs; Jill S Halterman; Lynn Olson; William M Vollmer; Rosalind J Wright; Virginia Taggart Journal: J Allergy Clin Immunol Date: 2012-03 Impact factor: 10.793
Authors: David Thissen; James W Varni; Brian D Stucky; Yang Liu; Debra E Irwin; Darren A Dewalt Journal: Qual Life Res Date: 2011-03-08 Impact factor: 4.147
Authors: Michael Schatz; David Mosen; Mark Kosinski; William M Vollmer; Elizabeth O'Connor; E Francis Cook; Robert S Zeiger Journal: Qual Life Res Date: 2006-10-11 Impact factor: 3.440
Authors: Patricia M Herman; Maria O Edelen; Anthony Rodriguez; Lara G Hilton; Ron D Hays Journal: BMC Musculoskelet Disord Date: 2020-10-10 Impact factor: 2.362