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.
Similar content being viewed by others
References
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.
Goldberg, D. S., & McGee, S. J. (2011). Pain as a global public health priority. BMC Public Health, 11, 770.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Cleeland, C. S. (2009). The brief pain inventory user guide. Retrieved 4/16/2015, fromhttp://www.mdanderson.org/education-and-research/departments-programs-and-labs/departments-and-divisions/symptom-research/symptom-assessment-tools/BPI_UserGuide.pdf
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.
Ware, J. E, Jr. (2000). SF-36 health survey update. Spine, 25(24), 3130–3139.
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.
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.
Muthén, L. K., & Muthén, B. O. (2006). Mplus. Los Angeles: Muthén & Muthén.
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.
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.
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.
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.
Hu, L., & Bentler, P. M. (1998). Fit Indices in covariance structure modeling: Sensitivity to underparameterization model misspecification. Psychological Methods, 3, 424–453.
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.
McDonald, R. P. (1999). Test theory: A unified treatment. New York: Psychology Press.
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.
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
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/
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.
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.
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.
Revelle, W. (2015). psych: Procedures for personality and psychological research (version 1.5.1). Evanston, IL: Northwestern University.
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.
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.
Cai, L., Thissen, D., & du Toit, S. (2011). IRTPRO 2.1 for Windows. Lincolnwood, IL: Scientific Software International Inc.
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.
Kolen, M. J., & Brennan, R. L. (2004). Test equating, scaling, and linking: Methods and practices. New York: Springer.
Lord, F. M. (1982). The standard error of equipercentile equating. Journal of Educational and Behavioral Statistics, 7(3), 165–174.
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).
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
Reinsch, C. H. (1967). Smoothing by spline functions. Numerische Mathematik, 10(3), 177–183.
Cai, L., Thissen, D., & du Toit, S. H. C. (2011). IRTPRO for Windows user’s guide. Lincolnwood, IL: Scientific Software International.
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.
Dorans, N. J. (2004). Equating, concordance, and expectation. Applied Psychological Measurement, 28(4), 227–246.
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.
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.
Dorans, N. J. (2007). Linking scores from multiple health outcome instruments. Quality of Life Research, 16(Suppl 1), 85–94.
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.
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.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Cook, K.F., Schalet, B.D., Kallen, M.A. et al. 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. Qual Life Res 24, 2305–2318 (2015). https://doi.org/10.1007/s11136-015-0987-6
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11136-015-0987-6