nach oben

Quality of Life Research

Erschienen in:

19.01.2018

Measurement invariance and general population reference values of the PROMIS Profile 29 in the UK, France, and Germany

verfasst von: Felix Fischer, Chris Gibbons, Joël Coste, Jose M. Valderas, Matthias Rose, Alain Leplège

Erschienen in: Quality of Life Research | Ausgabe 4/2018

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

Purpose

Comparability of patient-reported outcome measures over different languages is essential to allow cross-national research. We investigate the comparability of the PROMIS Profile 29, a generic health-related quality of life measure, in general population samples in the UK, France, and Germany and present general population reference values.

Methods

A web-based survey was simultaneously conducted in the UK (n = 1509), France (1501), and Germany (1502). Along with the PROMIS Profile 29, we collected sociodemographic information as well as the EQ-5D. We tested measurement invariance by means of multigroup confirmatory factor analysis (CFA). Differences in the health-related quality of life between countries were modeled by linear regression analysis. We present general population reference data for the included PROMIS domains utilizing plausible value imputation and quantile regression.

Results

Multigroup CFA of the PROMIS Profile 29 showed that factor means are insensitive to potential measurement bias except in one item. We observed significant differences in patient-reported health between countries, which could be partially explained by the differences in overall ratings of health. The physical function and pain interference scales showed considerable floor effects in the normal population in all countries.

Conclusions

Scores derived from the PROMIS Profile 29 are largely comparable across the UK, France, and Germany. Due to the use of plausible value imputation, the presented general population reference values can be compared to data collected with other PROMIS short forms or computer-adaptive tests.

Vorheriger Artikel The relationship between exercise dose and health-related quality of life with a phase III cardiac rehabilitation program

Nächster Artikel Measurement invariance of the WHOQOL-AGE questionnaire across three European countries

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Nur mit Berechtigung zugänglich

Basch, E. (2017). Patient-reported outcomes—Harnessing patients’ voices to improve clinical care. New England Journal of Medicine, 376(2), 105–108. https://doi.org/10.1056/NEJMp1002530.CrossRefPubMed

Snyder, C. F., Jensen, R. E., Segal, J. B., & Wu, A. W. (2013). Patient-reported outcomes (PROs) outcomes research. Medical Care, 51(8 Suppl 3), 73–79.CrossRef

Black, N., Burke, L., Forrest, C. B., Sieberer, U. R., Ahmed, S., Valderas, J. M., … Alonso, J. (2016). Patient-reported outcomes: Pathways to better health, better services, and better societies. Quality of Life Research, 25(5), 1103–1112. https://doi.org/10.1007/s11136-015-1168-3.CrossRefPubMed

McNamara, R. L., Spatz, E. S., Kelley, T. A., Stowell, C. J., Beltrame, J., Heidenreich, P., … Lewin, J. (2015). Standardized outcome measurement for patients with coronary artery disease: Consensus from the International Consortium for Health Outcomes Measurement (ICHOM). Journal of the American Heart Association, 4(5), e001767. https://doi.org/10.1161/JAHA.115.001767.CrossRefPubMedCentralPubMed

Ware, J. E., Kosinski, M., Gandek, B., & Aaronson, N. (1998). The factor structure of the SF-36 health survey in 10 countries: Results from the IQOLA project. Journal of Clinical Epidemiology, 51(11), 1159–1165.CrossRefPubMed

Bullinger, M., Alonso, J., Apolone, G., Leplège, A., & Sullivan, M. (1998). Translating health status questionnaires and evaluating their quality: The IQOLA project approach. Journal of Clinical Epidemiology, 51(11), 913–923.CrossRefPubMed

Szende, A., Janssen, B., & Cabases, J. (2014). Self-reported population health: An international perspective based on EQ-5D. Dordrecht: Springer.CrossRef

Beaton, D. E., Bombardier, C., Guillemin, F., & Ferraz, M. B. (2000). Guidelines for the process of cross-cultural adaptation of self-report measures. Spine, 25(24), 3186–3191.CrossRefPubMed

Alonso, J., Bartlett, S. J., Rose, M., Aaronson, N., Chaplin, J. E., Efficace, F., … Forrest, C. B. (2013). The case for an international patient-reported outcomes measurement information system (PROMIS) initiative. Health and Quality of Life Outcomes, 11(210), 1–5. https://doi.org/10.1186/1477-7525-11-210.

10.

Cella, D., Yount, S., Rothrock, N., Gershon, R., Cook, K. F., Reeve, B. B., … Rose, M. (2007). Developing the patient-reported outcomes measurement information system (PROMIS). Medical Care, 45(5), 3–11. Retrieved from http://journals.lww.com/lww-medicalcare/Abstract/2007/05001/Developing_the_Patient_Reported_Outcomes.1.aspx.

11.

Cella, D., Yount, S., Rothrock, N., & Gershon, R. (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), 3–11.CrossRef

12.

Reeve, B. B., Hays, R. D., Bjorner, J. B., Cook, K. F., Crane, P. K., Teresi, J. A., … Cella, D. (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. https://doi.org/10.1097/01.mlr.0000250483.85507.04.CrossRefPubMed

13.

Böhnke, J. R., & Lutz, W. (2014). Using item and test information to optimize targeted assessments of psychological distress. Assessment, 21(6), 679–693. https://doi.org/10.1177/1073191114529152.CrossRefPubMed

14.

Patient-Reported Outcomes Measurement Information System. (2013). PROMIS instrument development and validation scientific standards version 2.0. Retrieved March 20, 2016, from http://www.nihpromis.org/Documents/PROMISStandards_Vers2.0_Final.pdf.

15.

Choi, S. W., Schalet, B. D., 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. https://doi.org/10.1037/a0035768.CrossRefPubMedCentralPubMed

16.

Schalet, B. D., Cook, K. F., Choi, S. W., & Cella, D. (2014). Establishing a common metric for self-reported anxiety: Linking the MASQ, PANAS, and GAD-7 to PROMIS Anxiety. Journal of Anxiety Disorders, 28(1), 88–96. https://doi.org/10.1016/j.janxdis.2013.11.006.CrossRefPubMed

17.

Fischer, H. F., Tritt, K., Klapp, B. F., & Fliege, H. (2011). How to compare scores from different depression scales: Equating the Patient Health Questionnaire (PHQ) and the ICD-10-symptom rating (ISR) using item response. International Journal of Methods in Psychiatric Research, 20(4), 203–214. https://doi.org/10.1002/mpr.CrossRefPubMed

18.

Wahl, I., Löwe, B., Bjorner, J. B., Fischer, H. F., Langs, G., Voderholzer, U., … Rose, M. (2014). Standardization of depression measurement: A common metric was developed for 11 self-report depression measures. Journal of Clinical Epidemiology, 67(1), 73–86. https://doi.org/10.1016/j.jclinepi.2013.04.019.CrossRefPubMed

19.

Millsap, R. E. (2011). Statistical approaches to measurement invariance. New York: Routledge.

20.

Holland, P., & Wainer, H. (2012). Differential item functioning. Hillsdale: Lawrence Erlbaum Associates, Inc.

21.

Reeve, B. B., Wyrwich, K. W., Wu, A. W., Velikova, G., Terwee, C. B., Snyder, C. F., … Butt, Z. (2013). ISOQOL recommends minimum standards for patient-reported outcome measures used in patient-centered outcomes and comparative effectiveness research. Quality of Life Research. https://doi.org/10.1007/s11136-012-0344-y.

22.

Bartlett, S. J., Witter, J., Cella, D., & Ahmed, S. (2017). Montreal accord on patient-reported outcomes use series-paper 6: Creating national initiatives to support development and use-the PROMIS example. Journal of Clinical Epidemiology. https://doi.org/10.1016/j.jclinepi.2017.04.015.

23.

Wahl, I., Löwe, B., & Rose, M. (2011). Das Patient-Reported Outcomes Measurement Information System (PROMIS): Übersetzung der Item-Banken für Depressivität und Angst ins Deutsche. Klinische Diagnostik und Evaluation, 4, 236–261.

24.

Farin, E., Nagl, M., Gramm, L., Heyduck, K., & Glattacker, M. (2013). Development and evaluation of the PI-G: A three-scale measure based on the German translation of the PROMIS pain interference item bank. Quality of Life Research. https://doi.org/10.1007/s11136-013-0575-6.

25.

Liegl, G., Rose, M., Correia, H., Fischer, H. F., Kanlidere, S., Mierke, A., … Nolte, S. (2017). An initial psychometric evaluation of the German PROMIS v1.2 physical function item bank in patients with a wide range of health conditions. Clinical Rehabilitation, 26921551771429. https://doi.org/10.1177/0269215517714297.

26.

Paz, S. H., Spritzer, K. L., Morales, L. S., & Hays, R. D. (2013). Evaluation of the Patient-Reported Outcomes Information System (PROMIS(®)) Spanish-language physical functioning items. Quality of Life Research, 22(7), 1819–1830. https://doi.org/10.1007/s11136-012-0292-6.CrossRefPubMed

27.

Oude Voshaar, M. A. H., ten Klooster, P. M., Glas, C., Vonkeman, H. E., Taal, E., Krishnan, E., … van de Laar, M. A. (2014). Calibration of the promis physical function item bank in Dutch patients with rheumatoid arthritis. PLoS ONE, 9(3), e92367. https://doi.org/10.1371/journal.pone.0092367.CrossRefPubMedCentralPubMed

28.

Crins, M. H. P., Roorda, L. D., Smits, N., de Vet, H. C. W., Westhovens, R., Cella, D., … Terwee, C. B. (2015). Calibration and validation of the Dutch-flemish PROMIS pain interference item bank in patients with chronic pain. PLoS ONE, 10(7), e0134094. https://doi.org/10.1371/journal.pone.0134094.CrossRefPubMedCentralPubMed

29.

Fischer, H. F., Wahl, I., Nolte, S., Liegl, G., Brähler, E., Löwe, B., & Rose, M. (2016). Language-related differential item functioning between English and German PROMIS depression items is negligible. International Journal of Methods in Psychiatric Research, 26(4), e1530. https://doi.org/10.1002/mpr.1530.

30.

Hahn, E. A., DeWalt, D. A., Bode, R. K., Garcia, S. F., Devellis, R. F., Correia, H., & Cella, D. (2014). New English and Spanish social health measures will facilitate evaluating health determinants. Health Psychology: Official Journal of the Division of Health Psychology, American Psychological Association, 33(5), 490–499. https://doi.org/10.1037/hea0000055.CrossRef

31.

Choi, S. W., Reise, S. P., Pilkonis, P. A., Hays, R. D., & Cella, D. (2010). Efficiency of static and computer adaptive short forms compared to full-length measures of depressive symptoms. Quality of Life Research, 19(1), 125–136. https://doi.org/10.1007/s11136-009-9560-5.CrossRefPubMed

32.

Cella, D., Gershon, R., Lai, J.-S., Choi, S. W., Yount, S., Rothrock, N., … Rose, M. (2007). The future of outcomes measurement: Item banking, tailored short-forms, and computerized adaptive assessment. Quality of Life Research, 16(5 Suppl 1), 133–141. https://doi.org/10.1007/s11136-007-9204-6.CrossRefPubMed

33.

van Reenen, M., & Janssen, B. (2015). EQ-5D-5L user guide. Basic information on how to use the EQ-5D-5L instrument. Retrieved from https://euroqol.org/wp-content/uploads/2016/09/EQ-5D-5L_UserGuide_2015.pdf. Accessed on 16 Jan 2018.

34.

Valderas, J. M., & Alonso, J. (2008). Patient reported outcome measures: A model-based classification system for research and clinical practice. Quality of Life Research, 17(9), 1125–1135. https://doi.org/10.1007/s11136-008-9396-4.CrossRefPubMed

35.

Patient-Reported Outcomes Measurement Information System. (2013). PROMIS short form scoring manual. Retrieved March 21, 2016, from http://www.assessmentcenter.net/documents/PROMIS%20Profile%20Scoring%20Manual.pdf.

36.

Amtmann, D., Cook, K. F., Jensen, M. P., Chen, W., Choi, S., Revicki, D. A., … Callahan, L. (2010). Development of a PROMIS item bank to measure pain interference. Pain, 150(1), 173–182. https://doi.org/10.1016/j.pain.2010.04.025.Development.CrossRefPubMedCentralPubMed

37.

Hahn, E. A., Devellis, R. F., Bode, R. K., Garcia, S. F., Castel, L. D., Eisen, S. V., …Cella, D. (2010). Measuring social health in the patient-reported outcomes measurement information system (PROMIS): Item bank development and testing. Quality of Life Research, 19(7), 1035–1044. https://doi.org/10.1007/s11136-010-9654-0.CrossRefPubMedCentralPubMed

38.

Lai, J. S., Cella, D., Choi, S., Junghaenel, D. U., Christodoulou, C., Gershon, R., & Stone, A. (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. https://doi.org/10.1016/j.apmr.2010.08.033.CrossRefPubMedCentralPubMed

39.

Pilkonis, P. A., Choi, S. W., Reise, S. P., Stover, A. M., Riley, W. T., & Cella, D. (2011). Item banks for measuring emotional distress from the Patient-Reported Outcomes Measurement Information System (PROMIS®): Depression, anxiety, and anger. Assessment, 18(3), 263–283. https://doi.org/10.1177/1073191111411667.CrossRefPubMedCentralPubMed

40.

Rose, M., Bjorner, J. B., Gandek, B., Bruce, B., Fries, J. F., & Ware, J. E. (2014). The PROMIS physical function item bank was calibrated to a standardized metric and shown to improve measurement efficiency. Journal of Clinical Epidemiology, 67(5), 516–526. https://doi.org/10.1016/j.jclinepi.2013.10.024.CrossRefPubMedCentralPubMed

41.

Buysse, D. J., Yu, L., Moul, D. E., Germain, A., Stover, A., Dodds, N. E., … Pilkonis, P. A. (2010). Development and validation of patient-reported outcome measures for sleep disturbance and sleep-related impairments. Sleep, 33(6), 781–92.CrossRefPubMedCentralPubMed

42.

Schalet, B. D., Hays, R. D., Jensen, S. E., Beaumont, J. L., Fries, J. F., & Cella, D. (2016). Validity of PROMIS physical function measures in diverse clinical samples. Journal of Clinical Epidemiology, 73, 112–118. https://doi.org/10.1016/j.jclinepi.2015.08.039.CrossRefPubMedCentralPubMed

43.

Schalet, B. D., Pilkonis, P. A., Yu, L., Dodds, N., Johnston, K. L., Yount, S., … Cella, D. (2016). Clinical validity of PROMIS depression, anxiety, and anger across diverse clinical samples. Journal of Clinical Epidemiology, 73, 119–127. https://doi.org/10.1016/j.jclinepi.2015.08.036.CrossRefPubMedCentralPubMed

44.

Cella, D., Lai, J.-S., Jensen, S. E., Christodoulou, C., Junghaenel, D. U., Reeve, B. B., & Stone, A. A. (2016). Clinical validity of the PROMIS® fatigue item bank across diverse clinical samples. Journal of Clinical Epidemiology, 73, 128–134. https://doi.org/10.1016/j.jclinepi.2015.08.037.CrossRefPubMedCentralPubMed

45.

Cook, K. F., Jensen, S. E., Schalet, B. D., Beaumont, J. L., Amtmann, D., Czajkowski, S., … Cella, D. (2016). PROMIS® measures of pain, fatigue, negative affect, physical function and social function demonstrate clinical validity across a range of chronic conditions. Journal of Clinical Epidemiology, 73, 89–102. https://doi.org/10.1016/j.jclinepi.2015.08.038.CrossRefPubMedCentralPubMed

46.

Hahn, E. A., Beaumont, J. L., Pilkonis, P. A., Garcia, S. F., Magasi, S., DeWalt, D. A., & Cella, D. (2016). The PROMIS satisfaction with social participation measures demonstrate responsiveness in diverse clinical populations. Journal of Clinical Epidemiology, 73, 135–141. https://doi.org/10.1016/j.jclinepi.2015.08.034.CrossRefPubMedCentralPubMed

47.

Stone, A. A., Broderick, J. E., Junghaenel, D. U., Schneider, S., & Schwartz, J. E. (2015). PROMIS fatigue, pain intensity, pain interference, pain behavior, physical function, depression, anxiety, and anger scales demonstrate ecological validity. Journal of Clinical Epidemiology, 74, 194–206. https://doi.org/10.1016/j.jclinepi.2015.08.029.CrossRefPubMed

48.

Askew, R. L., Cook, K. F., Revicki, D. A., Cella, D., & Amtmann, D. (2016). Evidence from diverse clinical populations supported clinical validity of PROMIS pain interference and pain behavior. Journal of Clinical Epidemiology, 73, 103–111. https://doi.org/10.1016/j.jclinepi.2015.08.035.CrossRefPubMedCentralPubMed

49.

Beaumont, J. L., Cella, D., Phan, A. T., Choi, S., Liu, Z., & Yao, J. C. (2012). Comparison of health-related quality of life in patients with neuroendocrine tumors with quality of life in the general US population. Pancreas, 41(3), 461–466. https://doi.org/10.1097/MPA.0b013e3182328045.CrossRefPubMed

50.

Craig, B. M., Reeve, B. B., Brown, P. M., Cella, D., Hays, R. D., Lipscomb, J., … Revicki, D. A. (2014). US valuation of health outcomes measured using the PROMIS-29. Value in Health, 17(8), 846–853. https://doi.org/10.1016/j.jval.2014.09.005.CrossRefPubMedCentralPubMed

51.

Pearman, T. P., Beaumont, J. L., Cella, D., Neary, M. P., & Yao, J. (2016). Health-related quality of life in patients with neuroendocrine tumors: An investigation of treatment type, disease status, and symptom burden. Supportive Care in Cancer: Official Journal of the Multinational Association of Supportive Care in Cancer. https://doi.org/10.1007/s00520-016-3189-z.

52.

Yount, S. E., Beaumont, J. L., Chen, S.-Y., Kaiser, K., Wortman, K., Van Brunt, D. L., … Cella, D. (2016). Health-related quality of life in patients with idiopathic pulmonary fibrosis. Lung, 194(2), 227–234. https://doi.org/10.1007/s00408-016-9850-y.CrossRefPubMed

53.

Hinchcliff, M., Beaumont, J. L., Thavarajah, K., Varga, J., Chung, A., Podlusky, S., … Cella, D. (2011). Validity of two new patient-reported outcome measures in systemic sclerosis: Patient-reported outcomes measurement information system 29-item health profile and functional assessment of chronic illness therapy-dyspnea short form. Arthritis Care & Research, 63(11), 1620–1628. https://doi.org/10.1002/acr.20591.CrossRef

54.

Hinchcliff, M., Beaumont, J. L., Carns, M., Podlusky, S., Thavarajah, K., Varga, J., … Chang, R. W. (2015). Longitudinal evaluation of PROMIS-29 and FACIT-dyspnea short forms in systemic sclerosis. Journal of Rheumatology, 42(1), 64–72. https://doi.org/10.1530/ERC-14-0411.Persistent.CrossRefPubMed

55.

Kirwan, J. R., & Reeback, J. S. (1986). Using a modified Stanford Health Assessment Questionnaire to access disability in UK patients with rheumatoid arthritis. British Journal of Rheumatology, 25, 206–209. https://doi.org/10.1093/rheumatology/25.2.206.CrossRefPubMed

56.

Muthén, L. K., & Muthén, B. (n.d.). Mplus user’s guide. Los Angeles: Muthén & Muthén. https://doi.org/10.1111/j.1600-0447.2011.01711.x.

57.

Muthén, B. O., & Asparouhov, T. (2002). Latent variable analysis with categorical outcomes: Multiple-group and growth modeling in Mplus. Mplus Web Notes, 4.

58.

Wu, H., & Estabrook, R. (2016). Identification of confirmatory factor analysis models of different levels of invariance for ordered categorical outcomes. Psychometrika, 81(4), 1014–1045. https://doi.org/10.1007/s11336-016-9506-0.CrossRefPubMedCentralPubMed

59.

Millsap, R. E., & Tein, J. Y. (2004). Multivariate behavioral assessing factorial invariance in ordered-categorical measures. Multivariate Behavioral Research, 39(3), 479–515. https://doi.org/10.1207/S15327906MBR3903.CrossRef

60.

Brown, T. A., & Kenny, D. A. (2006). Confirmatory factor analysis for applied research. New York: Guilford Press.

61.

Hu, L., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1–55. https://doi.org/10.1080/10705519909540118.CrossRef

62.

Asparouhov, T., & Muthen, B. (2006). Robust chi square difference testing with mean and variance adjusted test statistics. Mplus Web Notes, 10, 1–6. Retrieved from http://www.statmodel.com/download/webnotes/webnote10.pdf.

63.

Davidov, E., Meuleman, B., Cieciuch, J., Schmidt, P., & Billiet, J. (2014). Measurement equivalence in cross-national research. Annual Review of Sociology, 40(1), 55–75. https://doi.org/10.1146/annurev-soc-071913-043137.CrossRef

64.

Chen, F. F. (2007). Sensitivity of goodness of fit indexes to lack of measurement invariance. Structural Equation Modeling: A Multidisciplinary Journal, 14(3), 464–504. https://doi.org/10.1080/10705510701301834.CrossRef

65.

Meade, A. W., Johnson, E. C., & Braddy, P. W. (2008). Power and sensitivity of alternative fit indices in tests of measurement invariance. Journal of Applied Psychology, 93(3), 568–592. https://doi.org/10.1037/0021-9010.93.3.568.CrossRefPubMed

66.

Sass, D. A., Schmitt, T. A., & Marsh, H. W. (2014). Evaluating model fit with ordered categorical data within a measurement invariance framework: A comparison of estimators. Structural Equation Modeling: A Multidisciplinary Journal, 21(2), 167–180. https://doi.org/10.1080/10705511.2014.882658.CrossRef

67.

Glas, C., Geerlings, H., van de Laar, M. A. F. J., & Taal, E. (2009). Analysis of longitudinal randomized clinical trials using item response models. Contemporary Clinical Trials, 30(2), 158–170. https://doi.org/10.1016/j.cct.2008.12.003.CrossRefPubMed

68.

Gorter, R., Fox, J.-P., & Twisk, J. (2015). Why item response theory should be used for longitudinal questionnaire data analysis in medical research. BMC Medical Research Methodology, 2, 1–12. https://doi.org/10.1186/s12874-015-0050-x.

69.

Levy, R., & Mislevy, R. J. (2016). Bayesian psychometric modeling. Boca Raton: CRC Press.

70.

Marshall, A., Altman, D. G., Holder, R. L., & Royston, P. (2009). Combining estimates of interest in prognostic modelling studies after multiple imputation: Current practice and guidelines. BMC Medical Research Methodology, 9, 57. https://doi.org/10.1186/1471-2288-9-57.CrossRefPubMedCentralPubMed

71.

Hao, L., & Naiman, D. Q. (2007). Quantile regression. Thousand Oaks: Sage.CrossRef

72.

R Development Core Team. (2008). R: A language and environment for statistical computing. Vienna: R Foundation for Statistical Computing.

73.

Pasek, J. (2016). weights: Weighting and weighted statistics. R package. Retrieved from http://cran.r-project.org/package=weights. Accessed 16 Jan 2018.

74.

Koenker, R. (2016). quantreg: Quantile regression. R package. Retrieved from http://cran.r-project.org/package=quantreg. Accessed 16 Jan 2018.

75.

Honaker, J., King, G., & Blackwell, M. (2011). AMELIA II: A program for missing data. Journal of Statistical Software, 45(7), 1–54.CrossRef

76.

Janssen, B., & Szende, A. (2014). Population norms for the EQ-5D. In A. Szende, B. Janssen & J. Cabases (Eds.), Self-reported population health: An international perspective based on EQ-5D (pp. 19–30). Dordrecht: Springer.CrossRef

77.

Chalmers, R. P., Counsell, A., & Flora, D. B. (2016). It might not make a big DIF. Educational and Psychological Measurement, 76(1), 114–140. https://doi.org/10.1177/0013164415584576.CrossRef

78.

Katz, P., Pedro, S., & Michaud, K. (2016). Performance of the PROMIS 29-item profile in rheumatoid arthritis, osteoarthritis, fibromyalgia, and systemic lupus erythematosus. Arthritis Care & Research. https://doi.org/10.1002/acr.

79.

Marsman, M., Maris, G., Bechger, T., & Glas, C. (2016). What can we learn from Plausible Values? Psychometrika. https://doi.org/10.1007/s11336-016-9497-x.PubMedCentralPubMed

80.

Cella, D., Riley, W., Stone, A., Northrock, N., Reeve, B. B., Yount, S., … Hays, R. D. (2010). Initial adult health item banks and first wave testing of the patient-reported outcomes measurement information system (PROMIS™) network: 2005–2008. Journal of Clinical Epidemiology, 63(11), 1179–1194. https://doi.org/10.1016/j.jclinepi.2010.04.011.Initial.CrossRefPubMedCentralPubMed

81.

Gorter, R., Fox, J.-P., Apeldoorn, A., & Twisk, J. (2016). The influence of measurement model choice for randomized controlled trial results. Journal of Clinical Epidemiology. https://doi.org/10.1016/j.jclinepi.2016.06.011.PubMed

82.

Thissen, D., & Wainer, H. (2001). Test scoring. Mahwah, NJ: Lawrence Erlbaum Associates.

83.

Liegl, G., Wahl, I., Berghöfer, A., Nolte, S., Pieh, C., Rose, M., & Fischer, H. F. (2016). Using PHQ-9 item parameters of a common metric resulted in similar depression scores compared to independent IRT model reestimation. Journal of Clinical Epidemiology, 71, 25–34. https://doi.org/10.1016/j.jclinepi.2015.10.006.CrossRefPubMed

Titel: Measurement invariance and general population reference values of the PROMIS Profile 29 in the UK, France, and Germany
verfasst von: Felix Fischer
Chris Gibbons
Joël Coste
Jose M. Valderas
Matthias Rose
Alain Leplège
Publikationsdatum: 19.01.2018
Verlag: Springer International Publishing
Erschienen in: Quality of Life Research / Ausgabe 4/2018
Print ISSN: 0962-9343
Elektronische ISSN: 1573-2649
DOI: https://doi.org/10.1007/s11136-018-1785-8

Springer Professional

Abstract

Purpose

Methods

Results

Conclusions

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Wirtschaft"

Weitere Artikel der Ausgabe 4/2018

Measurement invariance of the WHOQOL-AGE questionnaire across three European countries

Longitudinal study of quality of life among children with acute respiratory infection and cough

Conversion of standard retrospective patient-reported outcomes to momentary versions: cognitive interviewing reveals varying degrees of momentary compatibility

The relationship between exercise dose and health-related quality of life with a phase III cardiac rehabilitation program

Association between secondhand smoke exposure and quality of life in pregnant women and postpartum women and the consequences on the newborns

Quality of life of French Canadian parents raising a child with autism spectrum disorder and effects of psychosocial factors