Advertisement

Quality of Life Research

, Volume 24, Issue 10, pp 2305–2318 | Cite as

Establishing a common metric for self-reported pain: linking BPI Pain Interference and SF-36 Bodily Pain Subscale scores to the PROMIS Pain Interference metric

  • Karon F. Cook
  • Benjamin D. Schalet
  • Michael A. Kallen
  • Joshua P. Rutsohn
  • David Cella
Article

Abstract

Purpose

The study purposes were to mathematically link scores of the Brief Pain Inventory Pain Interference Subscale and the Short Form-36 Bodily Pain Subscale (legacy pain interference measures) to the NIH Patient-Reported Outcome Measurement Information System (PROMIS®) Pain Interference (PROMIS-PI) metric and evaluate results.

Methods

Linking was accomplished using both equipercentile and item response theory (IRT) methods. Item parameters for legacy items were estimated on the PROMIS-PI metric to allow for pattern scoring. Crosswalk tables also were developed that associated raw scores (summed or average) on legacy measures to PROMIS-PI scores. For each linking strategy, participants’ actual PROMIS-PI scores were compared to those predicted based on their legacy scores. To assess the impact of different sample sizes, we conducted random resampling with replacement across 10,000 replications with sample sizes of n = 25, 50, and 75.

Results

Analyses supported the assumption that all three scales were measuring similar constructs. IRT methods produced marginally better results than equipercentile linking. Accuracy of the links was substantially affected by sample size.

Conclusions

The linking tools (crosswalks and item parameter estimates) developed in this study are robust methods for estimating the PROMIS-PI scores of samples based on legacy measures. We recommend using pattern scoring for users who have the necessary software and score crosswalks for those who do not.

Keywords

Pain Pain measurement Patient outcome assessment Psychometric methods/scaling Item response theory Instrument calibration/equivalency among scales 

Notes

Acknowledgments

This research was part of the PROsetta Stone® project, which was funded by the National Institutes of Health/National Cancer Institute grant RC4CA157236 (David Cella, PI). For more information on PROsetta Stone, see www.prosettastone.org.

References

  1. 1.
    IASP Task Force on Taxonomy. (1994). Part III: Pain terms—A current list with definitions and notes on usage. In H. Merskey & N. Bogduk (Eds.), Classification of chronic pain (pp. 209–214). Seattle, WA: IASP Press.Google Scholar
  2. 2.
    Goldberg, D. S., & McGee, S. J. (2011). Pain as a global public health priority. BMC Public Health, 11, 770.PubMedCentralCrossRefPubMedGoogle Scholar
  3. 3.
    Johannes, C. B., Le, T. K., Zhou, X., Johnston, J. A., & Dworkin, R. H. (2010). The prevalence of chronic pain in United States adults: Results of an Internet-based survey. Journal of Pain, 11(11), 1230–1239.CrossRefPubMedGoogle Scholar
  4. 4.
    Institute of Medicine. (2012). Institute of Medicine (US) Committee on Advancing Pain Research, Care, and Education. Relieving pain in America: A blueprint for transforming prevention, care, education, and research. Washington, DC: National Academies Press.Google Scholar
  5. 5.
    Mystakidou, K., Parpa, E., Tsilika, E., Pathiaki, M., Gennatas, K., Smyrniotis, V., et al. (2007). The relationship of subjective sleep quality, pain, and quality of life in advanced cancer patients. Sleep, 30(6), 737–742.PubMedCentralPubMedGoogle Scholar
  6. 6.
    Ramstad, K., Jahnsen, R., Skjeldal, O. H., & Diseth, T. H. (2012). Parent-reported participation in children with cerebral palsy: The contribution of recurrent musculoskeletal pain and child mental health problems. Developmental Medicine and Child Neurology, 54(9), 829–835.CrossRefPubMedGoogle Scholar
  7. 7.
    Schirbel, A., Reichert, A., Roll, S., Baumgart, D. C., Buning, C., Wittig, B., et al.. (2010). Impact of pain on health-related quality of life in patients with inflammatory bowel disease. World Journal of Gastroenterology, 16(25), 3168–3177.PubMedCentralCrossRefPubMedGoogle Scholar
  8. 8.
    Dworkin, R. H., Turk, D. C., Farrar, J. T., Haythornthwaite, J. A., Jensen, M. P., Katz, N. P., et al. (2005). Core outcome measures for chronic pain clinical trials: IMMPACT recommendations. Pain, 113(1–2), 9–19.CrossRefPubMedGoogle Scholar
  9. 9.
    Edelen, M. O., & Saliba, D. (2010). Correspondence of verbal descriptor and numeric rating scales for pain intensity: An item response theory calibration. Journals of Gerontology. Series A, Biological Sciences and Medical Sciences, 65(7), 778–785.CrossRefGoogle Scholar
  10. 10.
    Askew, R. L., Kim, J., Chung, H., Cook, K. F., Johnson, K. L., & Amtmann, D. (2013). Development of a crosswalk for pain interference measured by the BPI and PROMIS pain interference short form. Quality of Life Research, 22(10), 2769–2776.CrossRefPubMedGoogle Scholar
  11. 11.
    Cleeland, C. S., Gonin, R., Hatfield, A. K., Edmonson, J. H., Blum, R. H., Stewart, J. A., et al. (1994). Pain and its treatment in outpatients with metastatic cancer. New England Journal of Medicine, 330(9), 592–596.CrossRefPubMedGoogle Scholar
  12. 12.
    Ware, J. E, Jr., & Sherbourne, C. D. (1992). The MOS 36-item short-form health survey (SF-36). I. Conceptual framework and item selection. Medical Care, 30(6), 473–483.CrossRefPubMedGoogle Scholar
  13. 13.
    Liu, H., Cella, D., Gershon, R., Shen, J., Morales, L. S., Riley, W., et al. (2010). Representativeness of the patient-reported outcomes measurement information system internet panel. Journal of Clinical Epidemiology, 63(11), 1169–1178.PubMedCentralCrossRefPubMedGoogle Scholar
  14. 14.
    Cook, K. F., Molton, I. R., & Jensen, M. P. (2011). Fatigue and aging with a disability. Archives of Physical Medicine and Rehabilitation, 92(7), 1126–1133.CrossRefPubMedGoogle Scholar
  15. 15.
    Molton, I., Cook, K. F., Smith, A. E., Amtmann, D., Chen, W. H., & Jensen, M. P. (2014). Prevalence and impact of pain in adults aging with a physical disability: Comparison to a US general population sample. Clinical Journal of Pain, 30(4), 307–315.CrossRefPubMedGoogle Scholar
  16. 16.
    Amtmann, D., Cook, K. F., Jensen, M. P., Chen, W. H., Choi, S., Revicki, D., et al. (2010). Development of a PROMIS item bank to measure pain interference. Pain, 150(1), 173–182.PubMedCentralCrossRefPubMedGoogle Scholar
  17. 17.
    Samejima, F. (1969). Estimation of latent ability using a response pattern of graded scores (Psychometric Monograph No. 17). Richmond, VA: Psychometric Society. Retrieved from http://www.psychometrika.org/journal/online/MN17.pdf.
  18. 18.
    Cleeland, C. S., Nakamura, Y., Mendoza, T. R., Edwards, K. R., Douglas, J., & Serlin, R. C. (1996). Dimensions of the impact of cancer pain in a four country sample: New information from multidimensional scaling. Pain, 67(2–3), 267–273.CrossRefPubMedGoogle Scholar
  19. 19.
    Mendoza, T. R., Chen, C., Brugger, A., Hubbard, R., Snabes, M., Palmer, S. N., et al. (2004). Lessons learned from a multiple-dose post-operative analgesic trial. Pain, 109(1–2), 103–109.CrossRefPubMedGoogle Scholar
  20. 20.
    Shulman, M. A., Myles, P. S., Chan, M. T., McIlroy, D. R., Wallace, S., & Ponsford, J. (2015). Measurement of disability-free survival after surgery. Anesthesiology, 122(3), 524–536.CrossRefPubMedGoogle Scholar
  21. 21.
    Stubbs, B., Eggermont, L., Patchay, S., & Schofield, P. (2014). Older adults with chronic musculoskeletal pain are at increased risk of recurrent falls and the brief pain inventory could help identify those most at risk. Geriatrics & Gerontology International. doi: 10.1111/ggi.12357.
  22. 22.
    Kroenke, K., Theobald, D., Wu, J., Tu, W., & Krebs, E. E. (2012). Comparative responsiveness of pain measures in cancer patients. Journal of Pain, 13(8), 764–772.PubMedCentralCrossRefPubMedGoogle Scholar
  23. 23.
  24. 24.
    Ware, J. E., Kosinski, M., & Keller, S. D. (1994). SF-36 physical and mental health summary scales: A users’ manual. Boston, MA: The Health Institute.Google Scholar
  25. 25.
    Ware, J. E, Jr. (2000). SF-36 health survey update. Spine, 25(24), 3130–3139.CrossRefPubMedGoogle Scholar
  26. 26.
    Ware, J. E., Snow, K. K., Kosinski, M., & Gandek, B. (1993). SF-36 health survey: Manual and interpretation guide. Boston, MA: The Health Institute, New England Medical Center.Google Scholar
  27. 27.
    Choi, S. W., Schalet, B., Cook, K. F., & Cella, D. (2014). Establishing a common metric for depressive symptoms: Linking the BDI-II, CES-D, and PHQ-9 to PROMIS depression. Psychological Assessment, 26(2), 513–527.Google Scholar
  28. 28.
    Muthén, L. K., & Muthén, B. O. (2006). Mplus. Los Angeles: Muthén & Muthén.Google Scholar
  29. 29.
    Lance, C., Butts, M., & Michels, L. (2006). The sources of four commonly reported cutoff criteria: What did they really say? Organizational Research Methods, 9, 202–220.CrossRefGoogle Scholar
  30. 30.
    West, S. G., Taylor, A. B., & Wu, W. (2012). Model fit and model selection in structural equation modeling. In R. H. Hoyle (Ed.), Handbook of structural equation modeling (pp. 209–231). New York, NY: Guilford Press.Google Scholar
  31. 31.
    Cook, K. F., Kallen, M. A., & Amtmann, D. (2009). Having a fit: Impact of number of items and distribution of data on traditional criteria for assessing IRT’s unidimensionality assumption. Quality of Life Research, 18(4), 447–460.PubMedCentralCrossRefPubMedGoogle Scholar
  32. 32.
    Browne, M. W., Cudeck, R., Bollen, K. A., & Long, K. S. (1993). Alternative ways of assessing model fit. In K. A. Bollen & J. S. Long (Eds.), Testing structural equation models (pp. 136–162). Newbury Park, CA: Sage.Google Scholar
  33. 33.
    Hu, L., & Bentler, P. M. (1998). Fit Indices in covariance structure modeling: Sensitivity to underparameterization model misspecification. Psychological Methods, 3, 424–453.CrossRefGoogle Scholar
  34. 34.
    Hu, L. T., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling, 6(1), 1–55.CrossRefGoogle Scholar
  35. 35.
    McDonald, R. P. (1999). Test theory: A unified treatment. New York: Psychology Press.Google Scholar
  36. 36.
    Zinbarg, R. E., Revelle, W., Yovel, I., & Li, W. (2005). Cronbach’s α, Revelle’s β, and McDonald’s ωh: Their relations with each other and two alternative conceptualizations of reliability. Psychometrika, 70, 123–133.CrossRefGoogle Scholar
  37. 37.
    Revelle, W. (2013). psych: Procedures for personality and psychological research (R package version 1.2.8) (computer software). Evanston, IL: Northwestern University. http://cran.r-project.org/web/packages/psych/index.html
  38. 38.
    R Development Core Team. (2011). R: A language and environment for statistical computing. Vienna: Austria R Foundation for Statistical Computing. http://www.r-project.org/
  39. 39.
    Deng, N., Guyer, R., & Ware, J. E, Jr. (2015). Energy, fatigue, or both? A bifactor modeling approach to the conceptualization and measurement of vitality. Quality of Life Research, 24(1), 81–93.CrossRefPubMedGoogle Scholar
  40. 40.
    Paap, M. C., Brouwer, D., Glas, C. A., Monninkhof, E. M., Forstreuter, B., Pieterse, M. E., et al. (2015). The St George’s Respiratory Questionnaire revisited: A psychometric evaluation. Quality of Life Research, 24(1), 67–79.CrossRefPubMedGoogle Scholar
  41. 41.
    Reise, S. P., Scheines, R., Widaman, K. F., & Haviland, M. G. (2012). Multidimensionality and structural coefficient bias in structural equation modeling: A bifactor perspective. Educational and Psychological Measurement, 73(1), 5–26.CrossRefGoogle Scholar
  42. 42.
    Revelle, W. (2015). psych: Procedures for personality and psychological research (version 1.5.1). Evanston, IL: Northwestern University.Google Scholar
  43. 43.
    Wainer, H., & Thissen, D. (1996). How is reliability related to the quality of test scores? What is the effect of local dependence on reliability? Educational Measure, 15, 22–29.CrossRefGoogle Scholar
  44. 44.
    Chen, W. H., & Thissen, D. (1997). Local dependence indices for item pairs using item response theory. Journal of Educational and Behavioral Statistics, 22, 265–289.CrossRefGoogle Scholar
  45. 45.
    Cai, L., Thissen, D., & du Toit, S. (2011). IRTPRO 2.1 for Windows. Lincolnwood, IL: Scientific Software International Inc.Google Scholar
  46. 46.
    Dorans, N. J., & Holland, P. W. (2000). Population invariance and the equatability of tests: Basic theory and the linear case. Journal of Educational Measurement, 37(4), 281–306.CrossRefGoogle Scholar
  47. 47.
    Kolen, M. J., & Brennan, R. L. (2004). Test equating, scaling, and linking: Methods and practices. New York: Springer.CrossRefGoogle Scholar
  48. 48.
    Lord, F. M. (1982). The standard error of equipercentile equating. Journal of Educational and Behavioral Statistics, 7(3), 165–174.CrossRefGoogle Scholar
  49. 49.
    Brennan, R. (2004). Linking with Equivalent Group or Single Group Design (LEGS) (version 2.0). Iowa City, IA: University of Iowa, Center for Advanced Studies in Measurement and Assessment (CASMA).Google Scholar
  50. 50.
    Albano, T. (2011). Equate: Statistical methods for test score equating (R package version 1.1-4). http://cran.opensourceresources.org/web/packages/equate/equate.pdf
  51. 51.
    Reinsch, C. H. (1967). Smoothing by spline functions. Numerische Mathematik, 10(3), 177–183.CrossRefGoogle Scholar
  52. 52.
    Cai, L., Thissen, D., & du Toit, S. H. C. (2011). IRTPRO for Windows user’s guide. Lincolnwood, IL: Scientific Software International.Google Scholar
  53. 53.
    Fayers, P. M., Hjermstad, M. J., Klepstad, P., Loge, J. H., Caraceni, A., Hanks, G. W., et al.. (2011). The dimensionality of pain: Palliative care and chronic pain patients differ in their reports of pain intensity and pain interference. Pain, 152(7), 1608–1620.CrossRefPubMedGoogle Scholar
  54. 54.
    Dorans, N. J. (2004). Equating, concordance, and expectation. Applied Psychological Measurement, 28(4), 227–246.Google Scholar
  55. 55.
    Reise, S. P., Scheines, R., Widaman, K. F., & Haviland, M. G. (2013). Multidimensionality and structural coefficient bias in structural equation modeling: A bifactor perspective. Educational and Psychological Measurement, 73(1), 5–26.CrossRefGoogle Scholar
  56. 56.
    Yost, K. J., Eton, D. T., Garcia, S. F., & Cella, D. (2011). Minimally important differences were estimated for six Patient-Reported Outcomes Measurement Information System-Cancer scales in advanced-stage cancer patients. Journal of Clinical Epidemiology, 64(5), 507–516.PubMedCentralCrossRefPubMedGoogle Scholar
  57. 57.
    Dorans, N. J. (2007). Linking scores from multiple health outcome instruments. Quality of Life Research, 16(Suppl 1), 85–94.CrossRefPubMedGoogle Scholar
  58. 58.
    Thissen, D., Varni, J. W., Stucky, B. D., Liu, Y., Irwin, D. E., & Dewalt, D. A. (2011). Using the PedsQL 3.0 asthma module to obtain scores comparable with those of the PROMIS pediatric asthma impact scale (PAIS). Quality of Life Research, 20(9), 1497–1505.PubMedCentralCrossRefPubMedGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Karon F. Cook
    • 1
  • Benjamin D. Schalet
    • 1
  • Michael A. Kallen
    • 1
  • Joshua P. Rutsohn
    • 1
  • David Cella
    • 1
  1. 1.Department of Medical Social SciencesNorthwestern University Feinberg School of MedicineChicagoUSA

Personalised recommendations