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Quality of Life Research

Erschienen in:

01.09.2013

Psychometric characteristics of daily diaries for the Patient-Reported Outcomes Measurement Information System (PROMIS^®): a preliminary investigation

verfasst von: Stefan Schneider, Seung W. Choi, Doerte U. Junghaenel, Joseph E. Schwartz, Arthur A. Stone

Erschienen in: Quality of Life Research | Ausgabe 7/2013

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Abstract

Purpose

The Patient-Reported Outcomes (PRO) Measurement Information System (PROMIS^®) has developed assessment tools for numerous PROs, most using a 7-day recall format. We examined whether modifying the recall period for use in daily diary research would affect the psychometric characteristics of several PROMIS measures.

Methods

Daily versions of short-forms for three PROMIS domains (pain interference, fatigue, depression) were administered to a general population sample (n = 100) for 28 days. Analyses used multilevel item response theory (IRT) models. We examined differential item functioning (DIF) across recall periods by comparing the IRT parameters from the daily data with the PROMIS 7-day recall IRT parameters. Additionally, we examined whether the IRT parameters for day-to-day within-person changes are invariant to those for between-person (cross-sectional) differences in PROs.

Results

Dimensionality analyses of the daily data suggested a single dimension for each PRO domain, consistent with PROMIS instruments. One-third of the daily items showed uniform DIF when compared with PROMIS 7-day recall, but the impact of DIF on the scale level was minor. IRT parameters for within-person changes differed from between-person parameters for 3 depression items, which were more sensitive for measuring change than between-person differences, but not for pain interference and fatigue items. Notably, mean scores from daily diaries were significantly lower than the PROMIS 7-day recall norms.

Conclusions

The results provide initial evidence supporting the adaptation of PROMIS measures for daily diary research. However, scores from daily diaries cannot be directly interpreted on PROMIS norms established for 7-day recall.

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Cella, D., Riley, W., Stone, A., Rothrock, N., Reeve, B., Yount, S., et al. (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. Journal of Clinical Epidemiology, 63(11), 1179–1194.PubMedCrossRef

DeWalt, D. A., Rothrock, N., Yount, S., & Stone, A. A. (2007). Evaluation of item candidates—The PROMIS qualitative item review. Medical Care, 45(5), S12–S21.PubMedCrossRef

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.PubMedCrossRef

Institute of Medicine. (2011). Leading health indicators for healthy people 2020: Letter Report. Washington, DC: National Academies Press.

Leidy, N. K., Wilcox, T. K., Jones, P. W., Murray, L., Winnette, R., Howard, K., et al. (2010). Development of the EXAcerbations of Chronic Obstructive Pulmonary Disease Tool (EXACT): A Patient-Reported Outcome (PRO) Measure. Value in Health, 13(8), 965–975.PubMedCrossRef

Jim, H. S., Small, B., Faul, L. A., Franzen, J., Apte, S., & Jacobsen, P. B. (2011). Fatigue, depression, sleep, and activity during chemotherapy: daily and intraday variation and relationships among symptom changes. Annals of Behavioral Medicine, 42(3), 321–333.PubMedCrossRef

Chapman, C. R., Donaldson, G. W., Davis, J. J., & Bradshaw, D. H. (2011). Improving individual measurement of postoperative pain: The Pain Trajectory. Journal of Pain, 12(2), 257–262.PubMedCrossRef

Begg, A., Drummond, G., & Tiplady, B. (2003). Assessment of postsurgical recovery after discharge using a pen computer diary. Anaesthesia, 58(11), 1101–1105.PubMedCrossRef

Broderick, J. E., Schneider, S., Schwartz, J. E., & Stone, A. A. (2010). Interference with activities due to pain and fatigue: Accuracy of ratings across different reporting periods. Quality of Life Research, 19(8), 1163–1170.PubMedCrossRef

10.

Broderick, J. E., Schwartz, J. E., Schneider, S., & Stone, A. A. (2009). Can end-of-day reports replace momentary assessment of pain and fatigue? Journal of Pain, 10(3), 274–281.PubMedCrossRef

11.

Broderick, J. E., Schwartz, J. E., Vikingstad, G., Pribbernow, M., Grossman, S., & Stone, A. A. (2008). The accuracy of pain and fatigue items across different reporting periods. Pain, 139(1), 146–157.PubMedCrossRef

12.

Schwarz, N. (2007). Retrospective and concurrent self-reports: The rationale for real-time data capture. In A. A. Stone, S. S. Shiffman, A. Atienza, & L. Nebeling (Eds.), The science of real-time data capture: Self-reports in health research (pp. 11–26). New York: Oxford University Press.

13.

Winkielman, P., Knauper, B., & Schwarz, N. (1998). Looking back at anger: Reference periods change the interpretation of emotion frequency questions. Journal of Personality and Social Psychology, 75(3), 719–728.PubMedCrossRef

14.

Roesch, S. C., Aldridge, A. A., Stocking, S. N., Villodas, F., Leung, Q., Bartley, C. E., et al. (2010). Multilevel factor analysis and structural equation modeling of daily diary coping data: Modeling trait and state variation. Multivariate Behav Res, 45(5), 767–789.PubMedCrossRef

15.

Mehta, P. D., & Neale, M. C. (2005). People are variables too: Multilevel structural equations modeling. Psychological Methods, 10(3), 259–284.PubMedCrossRef

16.

Zyphur, M. J., Kaplan, S. A., & Christian, M. S. (2008). Assumptions of cross-level measurement and structural invariance in the analysis of multilevel data: Problems and solutions. Group Dynamics-Theory Research and Practice, 12(2), 127–140.CrossRef

17.

Skrondal, A., & Rabe Hesketh, S. (2004). Generalized latent variable modeling: multilevel, longitudinal, and structural equation models. Boca Raton, FL: Chapman & Hall.CrossRef

18.

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.PubMedCrossRef

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.PubMedCrossRef

20.

Pilkonis, P. A., Choi, S. W., Reise, S. P., Stover, A. M., Riley, W. T., Cella, D., et al. (2011). Item banks for measuring emotional distress from the patient-reported outcomes measurement information system (PROMIS (R)): Depression, Anxiety, and Anger. Assessment, 18(3), 263–283.PubMedCrossRef

21.

Samejima, F. (1969). Estimation of Latent Ability Using a Response Pattern of Graded Scores. Psychometrika, 34, 100–114.

22.

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), S22–S31.PubMedCrossRef

23.

Muthén, L. K., & Muthén, B. O. (1998-2010). Mplus user’s guide (6th ed.). Los Angeles, CA: Muthén & Muthén.

24.

McDonald, R. P. (1982). Linear Versus Non-Linear Models in Item Response Theory. Applied Psychological Measurement, 6(4), 379–396.CrossRef

25.

Grilli, L., & Rampichini, C. (2007). Multilevel factor models for ordinal variables. Structural Equation Modeling-a Multidisciplinary Journal, 14(1), 1–25.

26.

Gregorich, S. E. (2006). Do self-report instruments allow meaningful comparisons across diverse population groups? Testing measurement invariance using the confirmatory factor analysis framework. Medical Care, 44(11), S78–S94.PubMedCrossRef

27.

Mellenberg, G. J. (1982). Contingency table models for assessing item bias. Journal of Educational Statistics, 7, 105–108.CrossRef

28.

Muthén, B. O., & Asparouhov, T. (2009). Beyond multilevel regression modeling: multilevel analysis in a general latent variable framework. In J. Hox & J. K. Roberts (Eds.), The Handbook of Advanced Multilevel Analysis (pp. 15–40). New York: Taylor and Francis.

29.

Woods, C. M. (2009). Empirical Selection of Anchors for Tests of Differential Item Functioning. Applied Psychological Measurement, 33(1), 42–57.CrossRef

30.

Stark, S., Chernyshenko, E. S., & Drasgow, F. (2006). Detecting differential item functioning with confirmatory factor analysis and item response theory: Toward a unified strategy. Journal of Applied Psychology, 91(6), 1292–1306.PubMedCrossRef

31.

Benjamini, Y., & Hochberg, Y. (1995). Controlling the False Discovery Rate - a Practical and Powerful Approach to Multiple Testing. Journal of the Royal Statistical Society Series B-Methodological, 57(1), 289–300.

32.

Stone, A. A., Schwartz, J. E., Broderick, J. E., & Shiffman, S. S. (2005). Variability of momentary pain predicts recall of weekly pain: A consequence of the peak (or salience) memory heuristic. Personality and Social Psychology Bulletin, 31(10), 1340–1346.PubMedCrossRef

33.

Schneider, S., Stone, A. A., Schwartz, J. E., & Broderick, J. E. (2011). Peak and End Effects in Patients’ Daily Recall of Pain and Fatigue: A Within-Subjects Analysis. Journal of Pain, 12(2), 228–235.PubMedCrossRef

34.

Noonan, V. K., Cook, K. F., Bamer, A. M., Choi, S. W., Kim, J., & Amtmann, D. (2011). Measuring fatigue in persons with multiple sclerosis: creating a crosswalk between the Modified Fatigue Impact Scale and the PROMIS Fatigue Short Form. Quality of Life Research.

35.

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.PubMedCrossRef

36.

Gibbons, L. E., Feldman, B. J., Crane, H. M., Mugavero, M., Willig, J. H., Patrick, D., et al. (2011). Migrating from a legacy fixed-format measure to CAT administration: calibrating the PHQ-9 to the PROMIS depression measures. Quality of Life Research, 20(9), 1349–1357.PubMedCrossRef

37.

Ackerman, T. A. (1992). A Didactic Explanation of Item Bias, Item Impact, and Item Validity from a Multidimensional Perspective. Journal of Educational Measurement, 29(1), 67–91.CrossRef

38.

Muthén, B. O., & Curran, P. J. (1997). General longitudinal modeling of individual differences in experimental designs: A latent variable framework for analysis and power estimation. Psychological Methods, 2(4), 371–402.CrossRef

39.

Cranford, J. A., Shrout, P. E., Iida, M., Rafaeli, E., Yip, T., & Bolger, N. (2006). A procedure for evaluating sensitivity to within-person change: Can mood measures in diary studies detect change reliably? Personality and Social Psychology Bulletin, 32(7), 917–929.PubMedCrossRef

Titel: Psychometric characteristics of daily diaries for the Patient-Reported Outcomes Measurement Information System (PROMIS®): a preliminary investigation
verfasst von: Stefan Schneider
Seung W. Choi
Doerte U. Junghaenel
Joseph E. Schwartz
Arthur A. Stone
Publikationsdatum: 01.09.2013
Verlag: Springer Netherlands
Erschienen in: Quality of Life Research / Ausgabe 7/2013
Print ISSN: 0962-9343
Elektronische ISSN: 1573-2649
DOI: https://doi.org/10.1007/s11136-012-0323-3

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Abstract

Purpose

Methods

Results

Conclusions

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