Skip to main content
Mijn bibliotheek
Ga naar:
Nieuws Boeken Tijdschriften Toetsvragen Web-tv Video Audio
Tips voor Mijn BSL
BSL Shop
Inloggen

Welkom bij Erasmus MC & Bohn Stafleu van Loghum

Erasmus MC heeft ervoor gezorgd dat je Mijn BSL eenvoudig en snel kunt raadplegen. Je kunt je links eenvoudig registreren. Met deze gegevens kun je thuis, of waar ook ter wereld toegang krijgen tot Mijn BSL.

» Andere revante producten voor Erasmus MC studenten en medewerkers.

Registreer

Om ook buiten de locaties van Erasmus MC, thuis bijvoorbeeld, van Mijn BSL gebruik te kunnen maken, moet je jezelf eenmalig registreren. Dit kan alleen vanaf een computer op een van de locaties van Erasmus MC.

Eenmaal geregistreerd kun je thuis of waar ook ter wereld onbeperkt toegang krijgen tot Mijn BSL.

Login

Als u al geregistreerd bent, hoeft u alleen maar in te loggen om onbeperkt toegang te krijgen tot Mijn BSL.

Inloggen
Top
Quality of Life Research
Gepubliceerd in:

18-06-2022

Accuracy of mixture item response theory models for identifying sample heterogeneity in patient-reported outcomes: a simulation study

Auteurs: Tolulope T. Sajobi, Lisa M. Lix, Lara Russell, David Schulz, Juxin Liu, Bruno D. Zumbo, Richard Sawatzky

Gepubliceerd in: Quality of Life Research | Uitgave 12/2022

Log in om toegang te krijgen
share
DELEN

Deel dit onderdeel of sectie (kopieer de link)

  • Optie A:
    Klik op de rechtermuisknop op de link en selecteer de optie “linkadres kopiëren”
  • Optie B:
    Deel de link per e-mail
    Link delen
publish
Publiceer nu

Abstract

Purpose

Mixture item response theory (MixIRT) models can be used to uncover heterogeneity in responses to items that comprise patient-reported outcome measures (PROMs). This is accomplished by identifying relatively homogenous latent subgroups in heterogeneous populations. Misspecification of the number of latent subgroups may affect model accuracy. This study evaluated the impact of specifying too many latent subgroups on the accuracy of MixIRT models.

Methods

Monte Carlo methods were used to assess MixIRT accuracy. Simulation conditions included number of items and latent classes, class size ratio, sample size, number of non-invariant items, and magnitude of between-class difference in item parameters. Bias and mean square error in item parameters and accuracy of latent class recovery were assessed.

Results

When the number of latent classes was correctly specified, the average bias and MSE in model parameters decreased as the number of items and latent classes increased, but specification of too many latent classes resulted in modest decrease (i.e., < 10%) in the accuracy of latent class recovery.

Conclusion

The accuracy of MixIRT model is largely influenced by the overspecification of the number of latent classes. Appropriate choice of goodness-of-fit measures, study design considerations, and a priori contextual understanding of the degree of sample heterogeneity can guide model selection.
vorige artikel Oral health-related quality of life in children with celiac disease
volgende artikel How do the number of missing daily diary days impact the psychometric properties and meaningful change thresholds arising from a weekly average summary score?
Bijlagen
Alleen toegankelijk voor geautoriseerde gebruikers
Literatuur
1.
Alemayehu, D., & Cappelleri, J. C. (2012). Conceptual and analytical considerations toward the use of patient-reported outcomes in personalized medicine. American Health & Drug Benefits, 5(5), 310–317.
2.
Black, N., Burke, L., Forrest, C. B., Sieberer, U. H., Ahmed, S., Valderas, J. M., Bartlett, S. J., & Alonso, J. (2016). Patient-reported outcomes: Pathways to better health, better services, and better societies. Quality of Life Research, 25(5), 1103–1112.PubMedCrossRef
3.
Gibbons, E., Black, N., Fallowfield, L., Newhouse, R., & Fitzpatrick, R. (2016). Essay 4: Patient-reported outcome measures and the evaluation of services. In R. Fitzpatrick & H. Barratt (Eds.), Challenges, solutions and future directions in the evaluation of service innovations in health care and public health. NIHR Journals Library.
4.
Lord, F. M. (1980). Applications of item response theory to practical testing problems. Lawrence Erlbaum Associates.
5.
Muthén, B. O. (1989). Latent variable modeling in heterogeneous populations. Psychometrika, 54, 557–585.CrossRef
6.
Mellenbergh, G. J. (1989). Item bias and item response theory. International Journal of Educational Research, 13, 127–143.CrossRef
7.
Meredith, W. (1964). Notes on factorial invariance. Psychometrika, 29, 177–185.CrossRef
8.
Meredith, W. (1993). Measurement invariance, factor analysis, and factorial invariance. Psychometrika, 58, 525–543.CrossRef
9.
Millsap, R. E. (2011). Statistical approaches to measurement invariance. Routledge.
10.
11.
12.
Teresi, J. A., & Fleishman, J. A. (2007). Differential item functioning and health assessment. Quality of Life Research, 16(Suppl 1), 33–42.PubMedCrossRef
13.
McHorney, C. A., & Fleishman, J. A. (2006). Assessing and understanding measurement equivalence in health outcome measures. Issues for further quantitative and qualitative inquiry. Medical Care, 44(11 Suppl 3), S205–S210.PubMedCrossRef
14.
Schmitt, N., & Kuljanin, G. (2008). Measurement invariance: Review of practice and implications. Human Resource Management Review, 18(4), 210–222.CrossRef
15.
Holland, P. W., & Thayer, D. T. (1988). Differential item functioning and the Mantel Haenszel procedure. In H. Wainer, H. I. Braun, & Educational Testing Service (Eds.), Test validity (pp. 129–145). NJ: L Erlbaum Associates.
16.
Crane, P. K., Gibbons, L. E., Jolley, L., & van Belle, G. (2006). Differential item functioning analysis with ordinal logistic regression techniques. DIFdetect and difwithpar. Medical Care, 44(11 Suppl 3), S115–S123.PubMedCrossRef
17.
Zumbo, B. D. (1999). A handbook on the theory and methods of differential item functioning (DIF): Logistic regression modeling as a unitary framework for binary and Likert-type (ordinal) item scores. Directorate of Human Resources Research and Evaluation, Department of National Defense.
18.
Swaminathan, H., & Rogers, H. J. (1990). Detecting differential item functioning using logistic regression procedures. Journal of Educational Measurement, 27, 361–370.CrossRef
19.
Wu, Q., & Lei, P-W. Using multi-group confirmatory factor analysis to detect differential item functioning when tests are multidimensional. Paper presented at the Annual Meeting of the National Council for Measurement in Education, San Diego: CA, 2009
20.
Gonzales-Roma, V., Hernandez, A., & Gomez-Benito, J. (2006). Power and Type-I error of the mean and covariance structure analysis model for detecting differential item functioning in graded response items. Multivariate Behavioral Research, 41(1), 29–53.CrossRef
21.
Holland, P. W., & Thayer, D. T. (1988). Differential item functioning and the Mantel-Haenszel procedure. In H. Wainer & H. I. Braun (Eds.), Test Validity (pp. 129–145). Lawrence Erlbaum Associates.
22.
DeMars, C. E. (2009). Modification of the Mantel-Haenszel and logistic regression DIF procedures to incorporate the SIBTEST regression correction. Journal of Educational Behavioral Statistics, 34, 149–170.CrossRef
23.
Shealy, R., & Stout, W. F. (1993). A model-based standardization approach that separates true bias/DIF from group differences and detects test bias/DIF as well as item bias/DIF. Psychometrika, 58, 159–194.CrossRef
24.
Güler, N., & Penfield, R. D. (2009). A comparison of logistic regression and contingency table methods for simultaneous detection of uniform and nonuniform DIF. Journal of Educational Measurement, 46(3), 314–329.CrossRef
25.
De Ayala, R. J. (2009). The theory and practice of item response theory. Guilford Press.
26.
Flowers, C. P., Oshima, T. C., & Raju, N. S. (1999). A description and demonstration of the polytomous-DFIT framework. Applied Psychological Measurement, 23, 309–326.CrossRef
27.
Rost, J. (1990). Rasch models in latent classes: An integration of two approaches to item analysis. Applied Pscyhological Measurement, 14(3), 271–282.CrossRef
28.
Mislevy, R. J., & Verhelst, N. (1990). Modeling item responses when different subjects employ different solution strategies. Psychometrika, 55, 195–215.CrossRef
29.
Sen, S., & Cohen, A. S. (2019). Applications of mixture IRT models: A literature review. Measurement: Interdisciplinary Research and Perspectives, 17(4), 177–191.
30.
Wu, X., Sawatzky, R., Hopman, W., Mayo, N., Sajobi, T. T., Liu, J., Prior, J., Papaioannou, A., Josse, R. G., Towheed, T., Davison, K. S., & Lix, L. M. (2017). Latent variable mixture models to test for differential item functioning: a population-based analysis. Health Qual Life Outcomes, 15, 102.PubMedPubMedCentralCrossRef
31.
Sawatzky, R., Ratner, P. A., Kopec, J. A., & Zumbo, B. D. (2012). Latent variable mixture models: A promising approach for the validation of patient-reported outcomes. Quality of Life Research, 21(4), 637–650.PubMedCrossRef
32.
Sawatzky, R., Russell, L. B., Sajobi, T. T., Lix, L. M., Kopec, J., & Zumbo, B. D. (2018). The use of latent variable mixture models to identify invariant items in test construction. Quality of Life Research, 27(7), 1747–1755.CrossRef
33.
34.
Samuelsen, K. M. (2008). Examining differential item functioning from a latent mixture perspective. In G. R. Hancock & K. M. Samuelsen (Eds.), Advances in latent variable mixture models (pp. 177–197). Information Age.
35.
Lu, R., & Jiao, H. (2009). Detecting DIF using the mixture Rasch model. Paper presented at the annual meeting of the National Council on Measurement in Education, San Diego, CA
36.
Li, F., Cohen, A. S., Kim, S. H., & Cho, S. J. (2009). Model selection methods for mixture dichotomous IRT models. Applied Psychological Measurement, 33, 353–373.CrossRef
37.
Maij-de Meij, A. M., Kelderman, H., & van der Flier, H. (2010). Improvement in detection of differential item functioning using a mixture item response theory model. Multivariate Behavioral Research, 45(6), 975–999.PubMedCrossRef
38.
Demars, C. E., & Lau, A. (2011). Differential item functioning in with latent classes: How accurately can we detect who is responding differentially? Educational Psychology & Measurement, 71(4), 597–616.CrossRef
39.
Sen, S., Cohen, A. S., & Kim, S. (2016). The impact of non-normality on extraction of spurious latent classes in mixture IRT models. Applied Psychological Measurement, 40(2), 98–113.PubMedCrossRef
40.
McLachlan, G., & Peel, D. (2000). Finite mixture models. Wiley series in probability and statistics. Wiley.CrossRef
41.
Celeux, G., Hurn, M., & Robert, C. P. (2000). Computational and inferential difficulties with mixture posterior distributions. Journal of the American Statistical Association, 95(451), 957–970.CrossRef
42.
Rousseau, J., & Mengersen, K. (2011). Asymptotic behaviour of the posterior distribution in overfitted mixture models. Journal of the Royal Statistical Society: B, 73(Part 5), 689–710.CrossRef
43.
Samejima, F. (1997). Graded response model. In W. J. van der Linden & R. K. Hambleton (Eds.), Handbook of modern item response theory (pp. 85–100). Springer.CrossRef
44.
Lubke, G. H., & Muthén, B. (2005). Investigating population heterogeneity with factor mixture models. Psychological Methods, 10, 21–39.PubMedCrossRef
45.
Baghaei, P., & Carstensen, C. H. (2013). Fitting the mixed Rasch model to a reading comprehension test: Identifying reader types. Practical Assessment, Research & Evaluation, 18(5), n5.
46.
Preinerstorfer, D., & Formann, A. K. (2011). Parameter recovery and model selection in mixed Rasch models. British Journal of Mathematical & Statistical Psychology, 65(2), 251–262.CrossRef
47.
Kutscher, T., Eid, M., & Crayen, C. (2019). Sample size requirements for applying mixed polytomous item response models: Results of a Monte Carlo simulation study. Frontiers in Psychology, 13(10), 2494.CrossRef
48.
Choi, I. H., Paek, I., & Cho, S. J. (2017). The impact of various class-distinction features on model selection in the mixture Rasch model. Journal of Experimental Education., 85(3), 411–424.CrossRef
49.
Jin, K. Y., & Wang, W. C. (2014). Item response theory models for performance decline during testing. Journal of Educational Measurement, 51, 178–200.CrossRef
50.
Zumbo, B. D., & Harwell, M. R. (1999). The methodology of methodological research: Analyzing the results of simulation experiments (Paper No. ESQBS99–2). University of Northern British Columbia. Edgeworth Laboratory for Quantitative Behavioral Science
51.
Muthén, L. K., & Muthén, B. O. (2017). Mplus: statistical analysis with latent variables: User’s Guide (Version 8). Mplus, 2017
52.
R Core Team. (2018). R: A Language and environment for statistical computing. R Foundation for Statistical Computing
53.
Bauer, D. J., & Curran, P. J. (2003). Distributional assumptions of growth mixture models: Implications for over-extraction of latent trajectory classes. Psychological Methods, 8, 338–363.PubMedCrossRef
54.
Alexeev, N., Templin, J., & Cohen, A. S. (2011). Spurious latent classes in the mixture Rasch model. Journal of Educational Measurement, 48, 313–332.CrossRef
55.
Nylund, K. L., Asparouhov, T., & Muthén, B. O. (2007). Deciding on the number of classes in latent class analysis and growth mixture modeling: A Monte Carlo simulation study. Structural Equation Model, 14, 535–569.CrossRef
56.
Muthén, B., Brown, C. H., Masyn, K., Jo, B., Khoo, S. T., Yang, C. C., et al. (2002). General growth mixture modeling for randomized preventive interventions. Biostatistics, 3, 459–475.PubMedCrossRef
57.
Lin, T. H., & Dayton, C. M. (1997). Model selection information criteria for non-nested latent class models. Journal of Educational and Behavioral Statistics, 22, 249–264.CrossRef
58.
Tein, J. Y., Coxe, S., & Cham, H. (2013). Statistical power to detect the correct number of classes in latent profile analysis. Structural Equation Modeling: A Multidisciplinary Journal, 20(4), 640–657.CrossRef
59.
Finch, W. H., & French, B. F. (2012). Parameter estimation with mixture item response theory models: A Monte Carlo comparison of maximum likelihood and Bayesian methods. Journal of Modern Applied Statistical Methods, 11(1), 167–178.CrossRef
60.
Cho, S.-J., Cohen, A. S., & Kim, S.-H. (2013). Markov Chain Monte Carlo estimation of a mixture item response model. Journal of Statistical Computation & Simulation, 83, 278–306.CrossRef
Metagegevens
Titel
Accuracy of mixture item response theory models for identifying sample heterogeneity in patient-reported outcomes: a simulation study
Auteurs
Tolulope T. Sajobi
Lisa M. Lix
Lara Russell
David Schulz
Juxin Liu
Bruno D. Zumbo
Richard Sawatzky
Publicatiedatum
18-06-2022
Uitgeverij
Springer International Publishing
Gepubliceerd in
Quality of Life Research / Uitgave 12/2022
Print ISSN: 0962-9343
Elektronisch ISSN: 1573-2649
DOI
https://doi.org/10.1007/s11136-022-03169-0

Andere artikelen Uitgave 12/2022

Health-related quality of life analysis in ovarian cancer clinical trials involving PARP inhibitors: a critical methodological perspective

  • Commentary

Discussion on the assessment of functioning in people under 18 years of age

  • Letter to the Editor

How important is subjective well-being for patients? A qualitative interview study of people with psoriasis

  • Open Access

The role of parental health and distress in assessing children’s health status

  • Open Access

Measurement properties and interpretability of the PROMIS item banks in stroke patients: a systematic review

  • Review

Oral health-related quality of life in children with celiac disease