Bryce B Reeve1,2, David Thissen3, Darren A DeWalt4, I-Chan Huang5,6, Yang Liu7, Brooke Magnus3, Hally Quinn3, Heather E Gross8, Pamela A Kisala9, Pengsheng Ni10, Stephen Haley10, M J Mulcahey11, Susie Charlifue12, Robin A Hanks13, Mary Slavin10, Alan Jette10, David S Tulsky9. 1. Department of Health Policy and Management, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, 1101-D McGavran-Greenberg Hall, 135 Dauer Drive, CB 7411, Chapel Hill, NC, 27599-7411, USA. bbreeve@email.UNC.edu. 2. Cecil G. Sheps Center for Health Services Research, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA. bbreeve@email.UNC.edu. 3. Department of Psychology and Neuroscience, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA. 4. Division of General Medicine and Clinical Epidemiology, Cecil G. Sheps Center for Health Services Research, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA. 5. Department of Health Outcomes and Policy, College of Medicine, University of Florida, Gainesville, FL, USA. 6. Department of Epidemiology and Cancer Control, St. Jude Children's Research Hospital, Memphis, TN, USA. 7. School of Social Sciences, Humanities, and Arts University of California, Merced, USA. 8. Cecil G. Sheps Center for Health Services Research, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA. 9. Center for Assessment Research and Translation, Department of Physical Therapy, College of Health Sciences, University of Delaware, Newark, DE, USA. 10. Health and Disability Research Institute, Boston University School of Public Health, Boston, MA, USA. 11. Department of Occupational Therapy, School of Health Professions, Thomas Jefferson University, Philadelphia, PA, USA. 12. Craig Hospital, Englewood, CO, USA. 13. Wayne State University School of Medicine, Detroit, MI, USA.
Abstract
PURPOSE: Research studies that measure health-related quality of life (HRQOL) in both children and adults and longitudinal studies that follow children into adulthood need measures that can be compared across these age groups. This study links the PROMIS pediatric and adult emotional distress measures using data from participants with diverse health conditions and disabilities. METHODS: Analyses were conducted and compared in two separate samples to confirm the stability of results. One sample (n = 874) included individuals aged 14-20 years with special health care needs and who require health services. The other sample (n = 641) included individuals aged 14-25 years who have a physical or cognitive disability. Participants completed both PROMIS pediatric and adult measures. Item response theory-based scores were linked using the linear approximation to calibrated projection. RESULTS: The estimated latent-variable correlation between pediatric and adult PROMIS measures ranged from 0.87 to 0.94. Regression coefficients β 0 (intercept) and β 1 (slope), and mean squared error are provided to transform scores from the pediatric to the adult measures, and vice versa. CONCLUSIONS: This study used a relatively new linking method, calibrated projection, to link PROMIS pediatric and adult measure scores, thus expanding the use of PROMIS measures to research that includes both populations.
PURPOSE: Research studies that measure health-related quality of life (HRQOL) in both children and adults and longitudinal studies that follow children into adulthood need measures that can be compared across these age groups. This study links the PROMIS pediatric and adult emotional distress measures using data from participants with diverse health conditions and disabilities. METHODS: Analyses were conducted and compared in two separate samples to confirm the stability of results. One sample (n = 874) included individuals aged 14-20 years with special health care needs and who require health services. The other sample (n = 641) included individuals aged 14-25 years who have a physical or cognitive disability. Participants completed both PROMIS pediatric and adult measures. Item response theory-based scores were linked using the linear approximation to calibrated projection. RESULTS: The estimated latent-variable correlation between pediatric and adult PROMIS measures ranged from 0.87 to 0.94. Regression coefficients β 0 (intercept) and β 1 (slope), and mean squared error are provided to transform scores from the pediatric to the adult measures, and vice versa. CONCLUSIONS: This study used a relatively new linking method, calibrated projection, to link PROMIS pediatric and adult measure scores, thus expanding the use of PROMIS measures to research that includes both populations.
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