Quality of Life Research

, Volume 20, Issue 9, pp 1497–1505 | Cite as

Using the PedsQL™ 3.0 asthma module to obtain scores comparable with those of the PROMIS pediatric asthma impact scale (PAIS)

  • David Thissen
  • James W. Varni
  • Brian D. Stucky
  • Yang Liu
  • Debra E. Irwin
  • Darren A. DeWalt



The National Institutes of Health’s Patient-Reported Outcomes Measurement Information System (PROMIS) has developed several scales measuring symptoms and function for use by the clinical research community. One advantage of PROMIS is the ability to link other scales to the PROMIS metric.


The objectives of this research are to provide evidence of validity for one of the PROMIS measures, the Pediatric Asthma Impact Scale (PAIS), and to link the PedsQL™ Asthma Symptoms Scale with the metric of the PAIS.


Descriptive statistics were computed describing the relationships among scores on the PAIS, the PedsQL™ Asthma Symptoms, Treatment, Worry, and Communication Scales, and the DISABKIDS Asthma Impact and Worry Scales for approximately 300 children ages 8–17. A novel linkage method based on item response theory (IRT), calibrated projection, was used to link scores on the PedsQL™ Asthma Symptoms Scale with the metric of the PAIS.


The PAIS exhibited strong convergent validity with the PedsQL™ Asthma Symptoms Scale, and less strong relations with the other five scales. The linkage system uses scores on the PedsQL™ Asthma Symptoms Scale to produce relatively precise score estimates on the metric of the PAIS.


Results of this study provide evidence for the validity of the PAIS, and a method to use scores on the PedsQL™ Asthma Symptoms Scale to estimate scores on the metric of the PAIS, in partial fulfillment of the PROMIS goal to provide a lingua franca for health-related quality of life.


PROMIS HRQOL PRO Scale development Pediatrics Asthma 



We would like to acknowledge the contribution of Harry A. Guess, MD, PhD to the conceptualization and operationalization of this research prior to his death. We are grateful to Li Cai for the theoretical development and implementation in software of the two-tier methods for item parameter estimation and the computation of scaled scores, and for his advice on their use in this project. This work was funded by the National Institutes of Health through the NIH Roadmap for Medical Research, Grant 1U01AR052181-01, and by SBIR contract HHSN-2612007-00013C with the National Cancer Institute of the National Institutes of Health. Information on the Patient-Reported Outcomes Measurement Information System (PROMIS) can be found at and

Conflict of interest

Dr. Varni holds the copyright and the trademark for the PedsQL™ and receives financial compensation from the Mapi Research Trust, which is a nonprofit research institute that charges distribution fees to for-profit companies that use the Pediatric Quality of Life Inventory™.


  1. 1.
    Ader, D. N. (2007). Developing the patient-reported outcomes measurement information system (PROMIS). Medical Care, 45(1), S1–S2.CrossRefGoogle Scholar
  2. 2.
    Cella, D., Yount, S., Rothrock, N., et al. (2007). The patient-reported outcomes measurement information system (PROMIS): Progress of an NIH roadmap cooperative group during its first two years. Medical Care, 45(Suppl 1), S3–S11.PubMedCrossRefGoogle Scholar
  3. 3.
    Irwin, D. E., Stucky, B. D., Thissen, D., et al. (2010). Sampling plan and patient characteristics of the PROMIS pediatrics large-scale survey. Quality of Life Research, 19, 585–594.PubMedCrossRefGoogle Scholar
  4. 4.
    Irwin, D., Stucky, B. D., Thissen, D., et al. (2010). An item response analysis of the pediatric PROMIS anxiety and depressive symptoms scales. Quality of Life Research, 19, 595–607.PubMedCrossRefGoogle Scholar
  5. 5.
    Varni, J. W., Stucky, B. D., Thissen, D., et al. (2010). PROMIS pediatric pain interference scale: An item response theory analysis of the pediatric pain item bank. Journal of Pain, 11, 1109–1119.PubMedCrossRefGoogle Scholar
  6. 6.
    DeWitt, E. M., Stucky, B. D., & Thissen, D. et al. Construction of the PROMIS Pediatric Physical Function Scales: Built using item response theory. Journal of Clinical Epidemiology (in press).Google Scholar
  7. 7.
    Yeatts, K., Stucky, B. D., Thissen, D., et al. (2010). Construction of the pediatric asthma impact scale (PAIS) for the patient reported outcomes measurement information system (PROMIS). Journal of Asthma, 47, 295–302.PubMedCrossRefGoogle Scholar
  8. 8.
    Chan, K. S., Mangione-Smith, R., Burwinkle, T. M., et al. (2005). The PedsQL™: Reliability and validity of the short-form generic core scales and asthma module. Medical Care, 43, 256–265.PubMedCrossRefGoogle Scholar
  9. 9.
    Guyatt, G. H., Juniper, E. F., Griffith, L. E., et al. (1997). Children and adult perceptions of childhood asthma. Pediatrics, 99, 165–168.PubMedCrossRefGoogle Scholar
  10. 10.
    Juniper, E. F., Guyatt, G. H., Feeny, D. H., et al. (1997). Measuring quality of life in children with asthma. Quality of Life Research, 5, 35–46.CrossRefGoogle Scholar
  11. 11.
    Varni, J. W., Burwinkle, T. M., Rapoff, M. A., et al. (2004). The PedsQL™ in pediatric asthma: Reliability and validity of the pediatric quality of life inventory™ generic core scales and asthma module. Journal of Behavioral Medicine, 27, 297–318.PubMedCrossRefGoogle Scholar
  12. 12.
    Irwin, D. E., Varni, J. W., Yeatts, K., et al. (2009). Cognitive interviewing methodology in the development of a pediatric item bank: A patient reported outcomes measurement information system (PROMIS) study. Health Qual Life Outcomes, 7, 1–10.CrossRefGoogle Scholar
  13. 13.
    DeWalt, D. A., Rothrock, N., Yount, S., et al. (2007). Evaluation of item candidates: The PROMIS qualitative item review. Medical Care, 45(Suppl 1), S12–S21.PubMedCrossRefGoogle Scholar
  14. 14.
    Walsh, T. R., Irwin, D. E., Meier, A., et al. (2008). The use of focus groups in the development of the PROMIS pediatrics item bank. Quality of Life Research, 17, 725–735.PubMedCrossRefGoogle Scholar
  15. 15.
    Samejima, F. (1969). Estimation of latent ability using a response pattern of graded scores. Psychometrika Monograph, 34(Monograph Suppl), 1–100.Google Scholar
  16. 16.
    Samejima, F. (1997). Graded response model. In W. J. van der Linden & R. K. Hambleton (Eds.), Handbook of modern item response theory (pp. 85–100). New York: Springer.Google Scholar
  17. 17.
    PROMIS Assessment Center web site. (2010) Available at Accessed 3 Sept 2010.
  18. 18.
    Baars, R. M., Atherton, C. I., Koopman, H. M., et al. (2005). The European DISABKIDS project: Development of seven condition-specific modules to measure health related quality of life in children and adolescents. Health Qual Life Outcomes, 3, 1–9.CrossRefGoogle Scholar
  19. 19.
    Thissen, D., Nelson, L., Rosa, K., et al. (2001). Item response theory for items scored in more than two categories. In D. Thissen & H. Wainer (Eds.), Test scoring (pp. 141–186). Mahwah, NJ: Lawrence Erlbaum Associates.Google Scholar
  20. 20.
    Holland, P. W. (2007). Framework and history for score linking. In N. J. Dorans, M. Pommerich, & P. W. Holland (Eds.), Linking and aligning scores and scales (pp. 5–30). New York: Springer.CrossRefGoogle Scholar
  21. 21.
    Dorans, N. J., & Holland, P. W. (2000). Population invariance and the equitability of tests: Basic theory and the linear case. Journal of Educational Measurement, 37, 281–306.CrossRefGoogle Scholar
  22. 22.
    IRTPRO. (2011). Flexible professional item response theory modeling for patient-reported outcomes [Computer program]. Chicago, IL: SSI International.Google Scholar
  23. 23.
    Cai, L. (2010). A two-tier full-information item factor analysis model with applications. Psychometrika, 75, 581–612.CrossRefGoogle Scholar
  24. 24.
    Green, B. F., Bock, R. D., Humphreys, L. G., et al. (1984). Technical guidelines for assessing computerized adaptive tests. Journal of Educational Measurement, 21, 347–360.CrossRefGoogle Scholar
  25. 25.
    Muraki, E. (1990). Fitting a polytomous item response model to likert-type data. Applied Psychological Measurement, 14, 59–71.CrossRefGoogle Scholar
  26. 26.
    McHorney, C. A., & Cohen, A. S. (2000). Equating health status measures with item response theory: Illustrations with functional status items. Medical Care, 38(Suppl), II43–II59.PubMedGoogle Scholar
  27. 27.
    Masse, L. C., Allen, D., Wilson, M., et al. (2006). Introducing equating methodologies to compare test scores from two different self-regulation scales. Health Education Research, 21(Suppl 1), i110–i120.PubMedCrossRefGoogle Scholar
  28. 28.
    Fisher, W. P., Jr., Eubanks, R. L., & Marier, R. L. (1997). Equating the MOS SF36 and the LSU HSI physical functioning scales. Journal of Outcome Measurement, 1, 329–362.PubMedGoogle Scholar
  29. 29.
    Bond, T. G., & Fox, C. M. (2007). Applying the Rasch model: Fundamental measurement in the human sciences. Mahwah, NJ: Lawrence Erlbaum Associates.Google Scholar
  30. 30.
    Holzner, B., Bode, R. K., Hahn, E. A., et al. (2006). Equating EORTC QLQ-C30 and FACT-G scores and its use in oncological research. European Journal of Cancer, 42, 3169–3177.PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2011

Authors and Affiliations

  • David Thissen
    • 1
  • James W. Varni
    • 2
    • 3
  • Brian D. Stucky
    • 1
  • Yang Liu
    • 1
  • Debra E. Irwin
    • 4
  • Darren A. DeWalt
    • 5
  1. 1.Department of Psychology, CB# 3270, Davie HallUniversity of North Carolina at Chapel HillChapel HillUSA
  2. 2.Department of Pediatrics, College of MedicineTexas A&M UniversityCollege StationUSA
  3. 3.Department of Landscape Architecture and Urban Planning, College of ArchitectureTexas A&M UniversityCollege StationUSA
  4. 4.Department of EpidemiologyUniversity of North Carolina at Chapel HillChapel HillUSA
  5. 5.Division of General Medicine and Clinical Epidemiology and Cecil G. Sheps Center for Health Services ResearchUniversity of North Carolina at Chapel HillChapel HillUSA

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