Likely change indexes improve estimates of individual change on patient-reported outcomes.
Cancer
Individual change
Meaningful change
Patient-reported outcomes
Journal
Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation
ISSN: 1573-2649
Titre abrégé: Qual Life Res
Pays: Netherlands
ID NLM: 9210257
Informations de publication
Date de publication:
May 2023
May 2023
Historique:
accepted:
07
07
2022
medline:
25
4
2023
pubmed:
4
8
2022
entrez:
3
8
2022
Statut:
ppublish
Résumé
Individual change on a patient-reported outcome (PRO) measure can be assessed by statistical significance and meaningfulness to patients. We explored the relationship between these two criteria by varying the confidence levels of the coefficient of repeatability (CR) on the Patient-Reported Outcomes Measurement Information System (R) Physical Function (PF) 10a (PF10a) measure. In a sample of 1129 adult cancer patients, we estimated individual-change thresholds on the PF10a from baseline to 6 weeks later with the CR at 50%, 68%, and 95% confidence. We also assessed agreement with group- and individual-level thresholds from anchor-based methods [mean change and receiver operating characteristic (ROC) curve] using a PF-specific patient global impression of change (PGIC). CRs at 50%, 68%, and 95% confidence were 3, 4, and 7 raw score points, respectively. The ROC- and mean-change-based thresholds for deterioration were -4 and -6; for improvement they were both 2. Kappas for agreement between anchor-based thresholds and CRs for deterioration ranged between κ = 0.65 and 1.00, while for improvement, they ranged between 0.35 and 0.83. Agreement between the PGIC and all CRs always fell below "good" (κ < 0.40) for deterioration (0.30-0.33) and were lower for improvement (0.16-0.28). In comparison to the CR at 95% confidence, CRs at 50% and 68% confidence (considered likely change indexes) have the advantage of maximizing the proportion of patients appropriately classified as changed according to statistical significance and meaningfulness.
Identifiants
pubmed: 35921034
doi: 10.1007/s11136-022-03200-4
pii: 10.1007/s11136-022-03200-4
pmc: PMC9994541
mid: NIHMS1858223
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
1341-1352Subventions
Organisme : NCI NIH HHS
ID : R01 CA154537
Pays : United States
Organisme : NCI NIH HHS
ID : U01CA233169
Pays : United States
Organisme : NIA NIH HHS
ID : P30 AG021684
Pays : United States
Organisme : NCI NIH HHS
ID : UG1 CA189828
Pays : United States
Organisme : NCI NIH HHS
ID : U01CA233169
Pays : United States
Commentaires et corrections
Type : ErratumIn
Informations de copyright
© 2022. The Author(s), under exclusive licence to Springer Nature Switzerland AG.
Références
McNemar, Q. (1958). On growth measurement. Educational and Psychological Measurement, 18(1), 47–55.
doi: 10.1177/001316445801800104
Lord, F. M. (1956). The measurement of growth. Educational and Psychological Measurement, 16(4), 421–437.
doi: 10.1177/001316445601600401
Cronbach, L. J., & Furby, L. (1970). How we should measure “change”: Or should we? Psychological Bulletin, 74(1), 68–80.
doi: 10.1037/h0029382
US Food and Drug Administration. (2019). Discussion document for patient-focused drug development public workshop on guidance 4: Incorporating clinical outcome assessments into endpoints for regulatory decision-making. Silver Spring, MD: United States Department of Health and Human Services.
Terwee, C. B., Peipert, J. D., Chapman, R., Lai, J. S., Terluin, B., Cella, D., Griffith, P., & Mokkink, L. B. (2021). Minimal important change (MIC): A conceptual clarification and systematic review of MIC estimates of PROMIS measures. Quality of Life Research, 30(10), 2729–2754.
doi: 10.1007/s11136-021-02925-y
pubmed: 34247326
pmcid: 8481206
Terluin, B., Eekhout, I., & Terwee, C. B. (2017). The anchor-based minimal important change, based on receiver operating characteristic analysis or predictive modeling, may need to be adjusted for the proportion of improved patients. Journal of Clinical Epidemiology, 83, 90–100.
doi: 10.1016/j.jclinepi.2016.12.015
pubmed: 28093262
Terluin, B., Eekhout, I., Terwee, C. B., & de Vet, H. C. (2015). Minimal important change (MIC) based on a predictive modeling approach was more precise than MIC based on ROC analysis. Journal of Clinical Epidemiology, 68(12), 1388–1396.
doi: 10.1016/j.jclinepi.2015.03.015
pubmed: 25913670
Norman, G. R., Stratford, P., & Regehr, G. (1997). Methodological problems in the retrospective computation of responsiveness to change: The lesson of Cronbach. Journal of Clinical Epidemiology, 50(8), 869–879.
doi: 10.1016/S0895-4356(97)00097-8
pubmed: 9291871
Hays, R. D., & Peipert, J. D. (2018). Minimally important differences do not identify responders to treatment. JOJ Sciences, 1(1).
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.
doi: 10.1177/0163278705275339
pubmed: 15851771
Moinpour, C. M., Donaldson, G. W., Davis, K. M., Potosky, A. L., Jensen, R. E., Gralow, J. R., Back, A. L., Hwang, J. J., Yoon, J., Bernard, D. L., Loeffler, D. R., Rothrock, N. E., Hays, R. D., Reeve, B. B., Smith, A. W., Hahn, E. A., & Cella, D. (2017). The challenge of measuring intra-individual change in fatigue during cancer treatment. Quality of Life Research, 26(2), 259–271.
doi: 10.1007/s11136-016-1372-9
pubmed: 27469506
King, M. T., Dueck, A. C., & Revicki, D. A. (2019). Can methods developed for interpreting group-level patient-reported outcome data be applied to individual patient management? Medical Care, 57, S38–S45.
doi: 10.1097/MLR.0000000000001111
pubmed: 30985595
pmcid: 6467500
Jacobson, N. S., & Truax, P. (1991). Clinical significance: A statistical approach to defining meaningful change in psychotherapy research. Journal of Consulting and Clinical Psychology, 59(1), 12–19.
doi: 10.1037/0022-006X.59.1.12
pubmed: 2002127
Cella, D., Bullinger, M., Scott, C., & Barofsky, I. (2002). Group vs individual approaches to understanding the clinical significance of differences or changes in quality of life. Mayo Clinic Proceedings, 77(4), 384–392.
doi: 10.4065/77.4.384
pubmed: 11936936
Donaldson, G. (2008). Patient-reported outcomes and the mandate of measurement. Quality of Life Research, 17(10), 1303–1313.
doi: 10.1007/s11136-008-9408-4
pubmed: 18953670
Lee, M. K., Schalet, B. D., Cella, D., Yost, K. J., Dueck, A. C., Novotny, P. J., & Sloan, J. A. (2020). Establishing a common metric for patient-reported outcomes in cancer patients: Linking patient reported outcomes measurement information system (PROMIS), numerical rating scale, and patient-reported outcomes version of the common terminology criteria for adverse events (PRO-CTCAE). J Patient Rep Outcomes, 4(1), 106.
doi: 10.1186/s41687-020-00271-0
pubmed: 33305344
pmcid: 7728866
Jensen, R. E., Potosky, A. L., Reeve, B. B., Hahn, E., Cella, D., Fries, J., Smith, A. W., Keegan, T. H. M., Wu, X.-C., Paddock, L., & Moinpour, C. M. (2015). Validation of the PROMIS physical function measures in a diverse US population-based cohort of cancer patients. Quality of Life Research, 24(10), 2333–2344.
doi: 10.1007/s11136-015-0992-9
pubmed: 25935353
pmcid: 5079641
Wahl, E., Gross, A., Chernitskiy, V., Trupin, L., Gensler, L., Chaganti, K., Michaud, K., Katz, P., & Yazdany, J. (2017). Validity and responsiveness of a 10-item patient-reported measure of physical function in a rheumatoid arthritis clinic population. Arthritis Care & Research, 69(3), 338–346.
doi: 10.1002/acr.22956
Oken, M. M., Creech, R. H., Tormey, D. C., Horton, J., Davis, T. E., McFadden, E. T., & Carbone, P. P. (1982). Toxicity and response criteria of the Eastern Cooperative Oncology Group. American Journal of Clinical Oncology, 5(6), 649–655.
doi: 10.1097/00000421-198212000-00014
pubmed: 7165009
Hays, R. D., & Peipert, J. D. (2021). Between-group minimally important change versus individual treatment responders. Quality of Life Research, 30(10), 2765–2772.
doi: 10.1007/s11136-021-02897-z
pubmed: 34129173
pmcid: 8204732
SAS Institute Inc. (2021). Plot ROC curve with cutpoint labeling and optimal cutpoint analysis. Retrieved September 29, 2021, from https://support.sas.com/kb/25/018.html
Youden, W. J. (1950). Index for rating diagnostic tests. Cancer, 3(1), 32–35.
doi: 10.1002/1097-0142(1950)3:1<32::AID-CNCR2820030106>3.0.CO;2-3
pubmed: 15405679
Cohen, J. (1960). A coefficient of agreement for nominal scales. Educational and Psychological Measurement, 20(1), 37–46.
doi: 10.1177/001316446002000104
Fleiss, J. L., Levin, B., & Paik, M. C. (2004). The measurement of interrater agreement. In Statistical methods for rates and proportions (pp. 598–626). John Wiley & Sons, Inc.
Terwee, C. B., Terluin, B., Knol, D. L., & de Vet, H. C. W. (2011). Combining clinical relevance and statistical significance for evaluating quality of life changes in the individual patient. Journal of Clinical Epidemiology, 64(12), 1465–1467.
doi: 10.1016/j.jclinepi.2011.06.015
pubmed: 22032756
Terwee, C. B., Roorda, L. D., Knol, D. L., De Boer, M. R., & De Vet, H. C. W. (2009). Linking measurement error to minimal important change of patient-reported outcomes. Journal of Clinical Epidemiology, 62(10), 1062–1067.
doi: 10.1016/j.jclinepi.2008.10.011
pubmed: 19230609
US Food and Drug Administration. (2009). Guidance for industry patient-reported outcome measures: Use in medical product development to support labeling claims. Rockville, MD: US Department of Health and Human Services.
US Food and Drug Administration. (2018). Discussion document for patient-focused drug development public workshop on guidance 3: Select, develop or modify fit-for-purpose clinical outcome assessments. Silver Spring, MD: United States Department of Health and Human Services.
Coon, C. D., & Cook, K. F. (2018). Moving from significance to real-world meaning: Methods for interpreting change in clinical outcome assessment scores. Quality of Life Research, 27(1), 33–40.
doi: 10.1007/s11136-017-1616-3
pubmed: 28620874
Nunnally, J. C. (1978). Psychometric theory (2nd ed.). McGraw-Hill.
Segawa, E., Schalet, B., & Cella, D. (2020). A comparison of computer adaptive tests (CATs) and short forms in terms of accuracy and number of items administrated using PROMIS profile. Quality of Life Research, 29(1), 213–221.
doi: 10.1007/s11136-019-02312-8
pubmed: 31595451
Cella, D., Riley, W., Stone, A., Rothrock, N., Reeve, B., Yount, S., Amtmann, D., Bode, R., Buysse, D., Choi, S., Cook, K., Devellis, R., DeWalt, D., Fries, J. F., Gershon, R., Hahn, E. A., Lai, J. S., Pilkonis, P., Revicki, D., & Goup, P. C. (2010). The Patient-Reported Outcomes Measurement Information System (PROMIS) developed and tested its first wave of adult self-reported health outcome item banks: 2005–2008. J Clin Epidemiol, 63(11), 1179–1194.
doi: 10.1016/j.jclinepi.2010.04.011
pubmed: 20685078
pmcid: 2965562
Terluin, B., Griffiths, P., van der Wouden, J. C., Ingelsrud, L. H., & Terwee, C. B. (2020). Unlike ROC analysis, a new IRT method identified clinical thresholds unbiased by disease prevalence. Journal of Clinical Epidemiology, 124, 118–125.
doi: 10.1016/j.jclinepi.2020.05.008
pubmed: 32438022
Fayers, P. M., & Hays, R. D. (2014). Don’t middle your MIDs: Regression to the mean shrinks estimates of minimally important differences. Quality of Life Research, 23(1), 1–4.
doi: 10.1007/s11136-013-0443-4
pubmed: 23722635