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Quality of Life Research

, Volume 26, Issue 2, pp 259–271 | Cite as

The challenge of measuring intra-individual change in fatigue during cancer treatment

  • Carol M. MoinpourEmail author
  • Gary W. Donaldson
  • Kimberly M. Davis
  • Arnold L. Potosky
  • Roxanne E. Jensen
  • Julie R. Gralow
  • Anthony L. Back
  • Jimmy J. Hwang
  • Jihye Yoon
  • Debra L. Bernard
  • Deena R. Loeffler
  • Nan E. Rothrock
  • Ron D. Hays
  • Bryce B. Reeve
  • Ashley Wilder Smith
  • Elizabeth A. Hahn
  • David Cella
Article

Abstract

Purpose

To evaluate how well three different patient-reported outcomes (PROs) measure individual change.

Methods

Two hundred and fourteen patients (from two sites) initiating first or new chemotherapy for any stage of breast or gastrointestinal cancer participated. The 13-item FACIT Fatigue scale, a 7-item PROMIS® Fatigue Short Form (PROMIS 7a), and the PROMIS® Fatigue computer adaptive test (CAT) were administered monthly online for 6 months. Reliability of measured change was defined, under a population mixed effects model, as the ratio of estimated systematic variance in rate of change to the estimated total variance of measured individual differences in rate of change. Precision of individual measured change, the standard error of measurement of change, was given by the square root of the rate-of-change sampling variance. Linear and quadratic models were examined up to 3 and up to 6 months.

Results

A linear model for measured change showed the following by 6 and 3 months, respectively: PROMIS CAT (0.363 and 0.342); PROMIS SF (0.408 and 0.533); FACIT (0.459 and 0.473). Quadratic models offered no noteworthy improvement over linear models. Both reliability and precision results demonstrate the need to improve the measurement of intra-individual change.

Conclusions

These results illustrate the challenge of reliably measuring individual change in fatigue with a level of confidence required for intervention. Optimizing clinically useful measurement of intra-individual differences over time continues to pose a challenge for PROs.

Keywords

Measured change Intra-individual change Fatigue Patient-reported outcomes Cancer 

Notes

Acknowledgments

Primary funding for the Clinical Study on Measuring Change in Fatigue was provided as part of a National Institutes of Health, Patient-Reported Outcomes Measurement Information System initiative for Grant # U01 AR057971 (Georgetown—4442-007-FHCRC): Arnold L. Potosky, PhD and Carol M. Moinpour, PhD, Co-Principal Investigators. The Patient-Reported Outcomes Measurement Information System® (PROMIS®) is an NIH Roadmap initiative to develop valid and reliable patient-reported outcome measures to be applicable across a wide range of chronic diseases and demographic characteristics. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Cancer Institute or the National Institutes of Health. PROMIS II was funded by cooperative agreements with a Statistical Center (Northwestern University, PI: David Cella, PhD, 1U54AR057951), a Technology Center (Northwestern University, PI: Richard C. Gershon, PhD, 1U54AR057943), a Network Center (American Institutes for Research, PI: Susan (San) D. Keller, PhD, 1U54AR057926), and thirteen Primary Research Sites which may include more than one institution (State University of New York, Stony Brook, PIs: Joan E. Broderick, PhD and Arthur A. Stone, PhD, 1U01AR057948; University of Washington, Seattle, PIs: Heidi M. Crane, MD, MPH, Paul K. Crane, MD, MPH, and Donald L. Patrick, PhD, 1U01AR057954; University of Washington, Seattle, PIs: Dagmar Amtmann, PhD and Karon Cook, PhD, 1U01AR052171; University of North Carolina, Chapel Hill, PI: Darren A. DeWalt, MD, MPH, 2U01AR052181; Children’s Hospital of Philadelphia, PI: Christopher B. Forrest, MD, PhD, 1U01AR057956; Stanford University, PI: James F. Fries, MD, 2U01AR052158; Boston University, PIs: Stephen M. Haley, PhD and David Scott Tulsky, PhD (University of Michigan, Ann Arbor), 1U01AR057929; University of California, Los Angeles, PIs: Dinesh Khanna, MD and Brennan Spiegel, MD, MSHS, 1U01AR057936; University of Pittsburgh, PI: Paul A. Pilkonis, PhD, 2U01AR052155; Georgetown University, PIs: Arnold L. Potosky, PhD and Carol M. Moinpour, PhD (Fred Hutchinson Cancer Research Center, Seattle),U01AR057971; Children’s Hospital Medical Center, Cincinnati, PI: Esi M. Morgan DeWitt, MD, MSCE, 17 1U01AR057940; University of Maryland, Baltimore, PI: Lisa M. Shulman, MD, 1U01AR057967; and Duke University, PI: Kevin P. Weinfurt, PhD, 2U01AR052186). NIH Science Officers on this project have included Deborah Ader, PhD, Vanessa Ameen, MD, Susan Czajkowski, PhD, Basil Eldadah, MD, PhD, Lawrence Fine, MD, DrPH, Lawrence Fox, MD, PhD, Lynne Haverkos, MD, MPH, Thomas Hilton, PhD, Laura Lee Johnson, PhD, Michael Kozak, PhD, Peter Lyster, PhD, Donald Mattison, MD, Claudia Moy, PhD, Louis Quatrano, PhD, Bryce B. Reeve, PhD, William Riley, PhD, Ashley Wilder Smith, PhD, MPH, Susana Serrate-Sztein,MD, Ellen Werner, PhD and James Witter, MD, PhD. This manuscript was reviewed by PROMIS reviewers before submission for external peer review. The authors would like to thank the patients who participated in this study and who contributed data over the study period. We also appreciate the assistance of two individuals who helped set up the Clinical Study at FredHutch/SCCA: Denise L. Albano, MS and Christina G. Galer, MS. Chin H. Boo and Marjorie E. Alhadeff assisted with collection of SCCA clinical data. Kathleen Kealey and Nancy Williamson at the Fred Hutchinson Cancer Research Center provided key administrative assistance for the conduct of this study. We also appreciated assistance from Tania Lobo for statistical support at Georgetown University regarding form submission rates. In addition, we would like to thank Richard Gerson, PhD, Director of the PROMIS Technical Center that supports the PROMIS Assessment Center as well as Assessment Center staff Monica A. Prudencio and Linda Carrizosa.

Funding

This work was supported by National Institutes of Health (Georgetown University, PIs: Arnold L. Potosky, PhD and Carol M. Moinpour, PhD (Fred Hutchinson Cancer Research Center, Seattle), U01AR057971.

Compliance with ethical standards

Conflict of interest

The authors have no conflicts of interest to disclose.

References

  1. 1.
    Bower, J. E., Bak, K., Berger, A., Breitbart, W., Escalante, C. P., Ganz, P. A., et al. (2014). Screening, assessment, and management of fatigue in adult survivors of cancer: An American Society of Clinical oncology clinical practice guideline adaptation. Journal of Clinical Oncology, 32(17), 1840–1850.CrossRefPubMedPubMedCentralGoogle Scholar
  2. 2.
    Berger, A. M., Lockhart, K., & Agrawal, S. (2009). Variability of patterns of fatigue and quality of life over time based on different breast cancer adjuvant chemotherapy regimens. Oncology Nursing Forum, 36(5), 563–570.CrossRefPubMedGoogle Scholar
  3. 3.
    Davis, K., & Cella, D. (2002). Assessing quality of life in oncology clinical practice: A review of barriers and critical success factors. Journal of Clinical Outcomes Management, 9(6), 327–332.Google Scholar
  4. 4.
    Davis, K. M., Lai, J. S., Hahn, E. A., & Cella, D. (2008). Conducting routine fatigue assessments for use in clinical oncology practice: Patient and provider perspectives. Supportive Care in Cancer, 16(4), 379–386.CrossRefPubMedGoogle Scholar
  5. 5.
    Alexander, S., Minton, O., & Stone, P. C. (2009). Evaluation of screening instruments for cancer-related fatigue syndrome in breast cancer survivors. Journal of Clinical Oncology, 27(8), 1197–1201.CrossRefPubMedGoogle Scholar
  6. 6.
    Snyder, C. F., Aaronson, N. K., Choucair, A. K., Elliott, T. E., Greenhalgh, J., Halyard, M. Y., et al. (2012). Implementing patient-reported outcomes assessment in clinical practice: A review of the options and considerations. Quality of Life Research, 21(8), 1305–1314.CrossRefPubMedGoogle Scholar
  7. 7.
    Wagner, L. I., Schink, J., Bass, M., Patel, S., Diaz, M. V., Rothrock, N., et al. (2015). Bringing PROMIS to practice: Brief and precise symptom screening in ambulatory cancer care. Cancer, 121(6), 927–934.CrossRefPubMedGoogle Scholar
  8. 8.
    Reeve, B. B. (2006). Special issues for building computerized-adaptive tests for measuring patient-reported outcomes: The NIH’s investment in new technology. Medical Care, 44(11 (Supplement 3)), S198–S204.CrossRefPubMedGoogle Scholar
  9. 9.
    Cella, D., Yount, S., Rothrock, N., Gershon, R., Cook, K., Reeve, B., 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(5 Suppl 1), S3–S11.CrossRefPubMedPubMedCentralGoogle Scholar
  10. 10.
    Nunnally, J. C. (1978). Psychometric theory (2nd ed.). New York, NY: McGraw-Hill Publishing Co.Google Scholar
  11. 11.
    Ware, J. E. J., Brook, R. H., Davies, A. R., & Lohr, K. N. (1981). Choosing measures of health status for individuals in general populations. American Journal of Public Health, 71(6), 620–625.CrossRefPubMedPubMedCentralGoogle Scholar
  12. 12.
    McHorney, C. A., & Tarlov, A. R. (1995). Individual-patient monitoring in clinical practice: Are available health status surveys adequate? Quality of Life Research, 4(4), 293–307.CrossRefPubMedGoogle Scholar
  13. 13.
    Reeve, B. B., Hays, R. D., Bjorner, J. B., Cook, K. F., Crane, P. K., Teresi, J. A., et al. (2007). Psychometric evaluation and calibration of health-related quality of life item banks: Plans for the Patient-Reported Outcomes Measurement Information System (PROMIS). Medical Care, 45(5 Suppl 1), S22–S31.CrossRefPubMedGoogle Scholar
  14. 14.
    Donaldson, G. W., & Moinpour, C. M. (2002). Individual differences in quality-of-life treatment response. Medical Care, 40(6 Suppl), III39–III53.PubMedGoogle Scholar
  15. 15.
    Hahn, E. A., Cella, D., Chassany, O., Fairclough, D. L., Wong, G. Y., Hays, R. D., et al. (2007). Precision of health-related quality-of-life data compared with other clinical measures. Mayo Clinic Proceedings, 82(10), 1244–1254.CrossRefPubMedGoogle Scholar
  16. 16.
    Fung, C. H., & Hays, R. D. (2008). Prospects and challenges in using patient-reported outcomes in clinical practice. Quality of Life Research, 17(10), 1297–1302.CrossRefPubMedPubMedCentralGoogle Scholar
  17. 17.
    Donaldson, G. (2008). Patient-reported outcomes and the mandate of measurement. Quality of Life Research, 17(10), 1303–1313.CrossRefPubMedGoogle Scholar
  18. 18.
    Ware, J. E, Jr. (2003). Conceptualization and measurement of health-related quality of life: Comments on an evolving field. Archives of Physical Medicine and Rehabilitation, 84(4 Suppl 2), S43–S51.CrossRefPubMedGoogle Scholar
  19. 19.
    Lai, J. S., Cella, D., Choi, S., Junghaenel, D. U., Christodoulou, C., Gershon, R., et al. (2011). How item banks and their application can influence measurement practice in rehabilitation medicine: A PROMIS fatigue item bank example. Archives of Physical Medicine and Rehabilitation, 92(10 Suppl), S20–S27.CrossRefPubMedPubMedCentralGoogle Scholar
  20. 20.
    Petersen, M. A., Aaronson, N. K., Arraras, J. I., Chie, W. C., Conroy, T., Costantini, A., et al. (2013). The EORTC computer-adaptive tests measuring physical functioning and fatigue exhibited high levels of measurement precision and efficiency. Journal of Clinical Epidemiology, 66(3), 330–339.CrossRefPubMedGoogle Scholar
  21. 21.
    Cronbach, L. J. (1951). Coefficient alpha and the internal structure of tests. Psychometrika, 16(3), 297–334.CrossRefGoogle Scholar
  22. 22.
    Singer, J. D., & Willett, J. B. (2003). Applied longitudinal data analysis: Modeling change and event occurrence. New York: Oxford University Press.CrossRefGoogle Scholar
  23. 23.
    Hays, R. D., Brodsky, M., Johnston, M. F., Spritzer, K. L., & Hui, K.-K. (2005). Evaluating the statistical significance of health-related quality-of-life change in individual patients. Evaluation and the Health Professions, 28(2), 160–171.CrossRefPubMedGoogle Scholar
  24. 24.
    Garcia, S. F., Cella, D., Clauser, S. B., Flynn, K. E., Lai, J.-S., Reeve, B. B., et al. (2007). Standardizing patient-reported outcomes assessment in cancer clinical trials: A Patient-Reported Outcomes Measurement Information System initiative. Journal of Clinical Oncology, 25(32), 5106–5112.CrossRefPubMedGoogle Scholar
  25. 25.
    Clauser, S. B., Ganz, P. A., Lipscomb, J., & Reeve, B. B. (2007). Patient-reported outcomes assessment in cancer trials: Evaluating and enhancing the payoff to decision making. Journal of Clinical Oncology, 25(32), 5049–5050.CrossRefPubMedGoogle Scholar
  26. 26.
    Yellen, S. B., Cella, D. F., Webster, K., Blendowski, C., & Edward, K. (1997). Measuring fatigue and other anemia-related symptoms with the Functional Assessment of Cancer Therapy (FACT) measurement system. Journal of Pain and Symptom Management, 13(2), 63–74.CrossRefPubMedGoogle Scholar
  27. 27.
    Cella, D., Lai, J. S., Chang, C. H., Peterman, A., & Slavin, M. (2002). Fatigue in cancer patients compared with fatigue in the general United States population. Cancer, 94(2), 528–538.CrossRefPubMedGoogle Scholar
  28. 28.
    Gershon, R., Rothrock, N. E., Hanrahan, R. T., Jansky, L. J., Harniss, M., & Riley, W. (2010). The development of a clinical outcomes survey research application: Assessment Center. Quality of Life Research, 19(5), 677–685.CrossRefPubMedPubMedCentralGoogle Scholar
  29. 29.
    Cella, D., Gershon, R., Lai, J. S., & Choi, S. (2007). The future of outcomes measurement: Item banking, tailored short-forms, and computerized adaptive assessment. Quality of Life Research, 16(1 (Supplement)), 133–141.CrossRefPubMedGoogle Scholar
  30. 30.
    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.CrossRefPubMedPubMedCentralGoogle Scholar
  31. 31.
    Cella, D., Hahn, E. A., & Dineen, K. (2002). Meaningful change in cancer-specific quality of life scores: Differences between improvement and worsening. Quality of Life Research, 11(3), 207–221.CrossRefPubMedGoogle Scholar
  32. 32.
    Buysse, D. J., Yu, L., Moul, D. E., Germain, Anne, Stover, A., Dodds, N. E., et al. (2010). Development and validation of patient-reported outcome measures for sleep disturbance and sleep-related impairments. Sleep, 33(6), 781–792.PubMedPubMedCentralGoogle Scholar
  33. 33.
    Hays, R. D., Bjorner, J. B., Revicki, D. A., Spritzer, K. L., & Calla, D. (2009). Development of physical and mental health summary scores from the patient-reported outcomes measurement information system (PROMIS) global items. Quality of Life Research, 18(7), 873–880.CrossRefPubMedPubMedCentralGoogle Scholar
  34. 34.
    Weaver, K. E., Forsythe, L. P., Reeve, B. B., Alfano, C. M., Rodriguez, J. L., Sabatino, S. A., et al. (2012). Mental and physical health-related quality of life among U.S. cancer survivors: Population estimates from the 2010 National Health Interview Survey. Cancer Epidemiology, Biomarkers and Prevention, 21(11), 2108–2117.CrossRefPubMedPubMedCentralGoogle Scholar
  35. 35.
    Basen-Engquist, K., Bodurka-Bovers, D., Fitzgerald, M. A., Webster, K., Cella, D., Hu, S., et al. (2001). Reliability and validity of the Functional Assessment of Cancer Therapy—Ovarian. Journal of Clinical Oncology, 19(6), 1809–1817.PubMedGoogle Scholar
  36. 36.
    Heffernan, N., Cella, D., Webster, K., Odom, L., Martone, M., Passik, S., et al. (2002). Measuring health-related quality of life in patients with hepatobiliary cancers: The functional assessment of cancer therapy—Hepatobiliary questionnaire. Journal of Clinical Oncology, 20(9), 2229–2239.CrossRefPubMedGoogle Scholar
  37. 37.
    Stewart, A. L., Hays, R. D., Wells, K. B., Rogers, W. H., Spritzer, K. L., & Greenfield, S. (1994). Long-term functioning and well-being outcomes associated with physical activity and exercise in patients with chronic conditions in the medical outcomes study. Journal of Clinical Epidemiology, 47(7), 719–730.CrossRefPubMedGoogle Scholar
  38. 38.
    Moinpour, C. M., Lovato, L. C., Thompson, I. M, Jr., Ware, J. E, Jr., Ganz, P. A., Patrick, D. L., et al. (2000). Profile of men randomized to the prostate cancer prevention trial: Baseline health-related quality of life, urinary and sexual functioning, and health behaviors. Journal of Clinical Oncology, 18(9), 1942–1953.PubMedGoogle Scholar
  39. 39.
    Laird, N. M., & Ware, J. W. (1982). Random-effects models for longitudinal data. Biometrics, 38(4), 963–974.CrossRefPubMedGoogle Scholar
  40. 40.
    Raudenbush, S. W., & Bryk, A. S. (2002). Hierarchical linear models. Applications and data analysis methods (2nd ed.). Thousand Oaks, CA: Sage Publications, Inc.Google Scholar
  41. 41.
    Cronbach, L. J., & Furby, L. (1970). How we should measure “change”: Or should we? Psychological Bulletin, 74(1), 68–80.CrossRefGoogle Scholar
  42. 42.
    Yost, K. J., & Eton, D. T. (2005). Combining distribution- and anchor-based approaches to determine minimally important differences: The FACIT experience. Evaluation and the Health Professions, 28(2), 172–191.CrossRefPubMedGoogle Scholar
  43. 43.
    Hendrikx, J., Fransen, J., Kievit, W., & van Riel, P. L. (2015). Individual patient monitoring in daily clinical practice: A critical evaluation of minimal important change. Quality of Life Research, 24(3), 607–616.CrossRefPubMedGoogle Scholar
  44. 44.
    Faes, C., Molenberghs, G., Aerts, M., Verbeke, G., & Kenward, M. G. (2009). The effective sample size and an alternative small-sample degrees-of-freedom method. The American Statistician, 63(4), 389–399.CrossRefGoogle Scholar
  45. 45.
    Brandmaier, A. M., von Oertzen, T., Ghisletta, P., Hertzog, C., & Lindenberger, U. (2015). LIFESPAN: A tool for the computer-aided design of longitudinal studies. Frontiers in Psychology, 6, 272. doi: 10.3389/fpsyg.2015.00272.CrossRefPubMedPubMedCentralGoogle Scholar
  46. 46.
    Skrondal, A., & Rabe-Hesketh, S. (2004). Generalized latent modeling: Multilevel, longitudinal, and structural equation models. Boca-Raton, FL: Chapmen & Hall/CRC.CrossRefGoogle Scholar
  47. 47.
    Lord, F. M., Novick, M. R., & Birnbaum, A. (1968). Statistical theories of mental test scores. Reading, MA: Addison-Wesley.Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Carol M. Moinpour
    • 1
    Email author
  • Gary W. Donaldson
    • 2
  • Kimberly M. Davis
    • 3
  • Arnold L. Potosky
    • 3
  • Roxanne E. Jensen
    • 3
  • Julie R. Gralow
    • 4
  • Anthony L. Back
    • 4
  • Jimmy J. Hwang
    • 5
  • Jihye Yoon
    • 6
  • Debra L. Bernard
    • 4
  • Deena R. Loeffler
    • 7
  • Nan E. Rothrock
    • 11
  • Ron D. Hays
    • 8
  • Bryce B. Reeve
    • 9
  • Ashley Wilder Smith
    • 10
  • Elizabeth A. Hahn
    • 11
  • David Cella
    • 11
  1. 1.Public Health Sciences DivisionFred Hutchinson Cancer Research Center [Emerita]SeattleUSA
  2. 2.Pain Research Center, Department of AnesthesiologyUniversity of UtahSalt Lake CityUSA
  3. 3.Health Services ResearchGeorgetown University Medical Center, and Georgetown University Lombardi Comprehensive Cancer CenterWashingtonUSA
  4. 4.Seattle Cancer Care AllianceFred Hutchinson Cancer Research CenterSeattleUSA
  5. 5.Hematology/OncologyLevine Cancer Institute, Carolinas HealthCare SystemCharlotteUSA
  6. 6.Cancer Prevention Program, Public Health Sciences DivisionFred Hutchinson Cancer Research CenterSeattleUSA
  7. 7.The Cystic Fibrosis FoundationBethesdaUSA
  8. 8.Departments of Medicine and Health Services ResearchUniversity of California Los AngelesLos AngelesUSA
  9. 9.Department of Health Policy and Management, Gillings School of Global Public HealthUniversity of North Carolina at Chapel HillChapel HillUSA
  10. 10.Outcomes Research BranchNational Cancer InstituteBethesdaUSA
  11. 11.Department of Medical Social Sciences and Center for Patient-Centered OutcomesNorthwestern University Feinberg School of MedicineChicagoUSA

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