Skip to main content

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

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.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2

References

  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.

    Google Scholar 

  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.

    CAS  PubMed  Article  Google Scholar 

  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.

    Google Scholar 

  4. Lord, F. M. (1980). Applications of item response theory to practical testing problems. Lawrence Erlbaum Associates.

    Google Scholar 

  5. Muthén, B. O. (1989). Latent variable modeling in heterogeneous populations. Psychometrika, 54, 557–585.

    Article  Google Scholar 

  6. Mellenbergh, G. J. (1989). Item bias and item response theory. International Journal of Educational Research, 13, 127–143.

    Article  Google Scholar 

  7. Meredith, W. (1964). Notes on factorial invariance. Psychometrika, 29, 177–185.

    Article  Google Scholar 

  8. Meredith, W. (1993). Measurement invariance, factor analysis, and factorial invariance. Psychometrika, 58, 525–543.

    Article  Google Scholar 

  9. Millsap, R. E. (2011). Statistical approaches to measurement invariance. Routledge.

    Google Scholar 

  10. Teresi, J. A., Wang, C., Kleinman, M., Jones, R. N., & Weiss, D. J. (2021). Differential item functioning analyses of the patient-reported outcomes measurement information system (PROMIS®) Measures: methods, challenges, advances, and future directions. Psychometrika, 86, 674–711. https://doi.org/10.1007/s11336-021-09775-0

    Article  PubMed  PubMed Central  Google Scholar 

  11. Meredith, W., & Teresi, J. A. (2006). An essay on measurement and factorial invariance. Medical Care, 44(Suppl 3), S69–S77. https://doi.org/10.1097/01.mlr.0000245438.73837.89

    Article  PubMed  Google Scholar 

  12. Teresi, J. A., & Fleishman, J. A. (2007). Differential item functioning and health assessment. Quality of Life Research, 16(Suppl 1), 33–42.

    PubMed  Article  Google Scholar 

  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.

    PubMed  Article  Google Scholar 

  14. Schmitt, N., & Kuljanin, G. (2008). Measurement invariance: Review of practice and implications. Human Resource Management Review, 18(4), 210–222.

    Article  Google Scholar 

  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.

    Google Scholar 

  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.

    PubMed  Article  Google Scholar 

  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.

    Google Scholar 

  18. Swaminathan, H., & Rogers, H. J. (1990). Detecting differential item functioning using logistic regression procedures. Journal of Educational Measurement, 27, 361–370.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  25. De Ayala, R. J. (2009). The theory and practice of item response theory. Guilford Press.

    Google Scholar 

  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.

    Article  Google Scholar 

  27. Rost, J. (1990). Rasch models in latent classes: An integration of two approaches to item analysis. Applied Pscyhological Measurement, 14(3), 271–282.

    Article  Google Scholar 

  28. Mislevy, R. J., & Verhelst, N. (1990). Modeling item responses when different subjects employ different solution strategies. Psychometrika, 55, 195–215.

    Article  Google Scholar 

  29. Sen, S., & Cohen, A. S. (2019). Applications of mixture IRT models: A literature review. Measurement: Interdisciplinary Research and Perspectives, 17(4), 177–191.

    Google Scholar 

  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.

    PubMed  PubMed Central  Article  Google Scholar 

  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.

    PubMed  Article  Google Scholar 

  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.

    Article  Google Scholar 

  33. Samuelsen, K. M. (2005).Examining differential item functioning from a latent class perspective. PhD Thesis, University of Maryland; Retrieved January 29, 2019 from http://gradworks.umi.com/31/75/3175148.html.

  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.

    Google Scholar 

  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.

    Article  Google Scholar 

  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.

    PubMed  Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    PubMed  Article  Google Scholar 

  40. McLachlan, G., & Peel, D. (2000). Finite mixture models. Wiley series in probability and statistics. Wiley.

    Book  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Chapter  Google Scholar 

  44. Lubke, G. H., & Muthén, B. (2005). Investigating population heterogeneity with factor mixture models. Psychological Methods, 10, 21–39.

    PubMed  Article  Google Scholar 

  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.

    Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  49. Jin, K. Y., & Wang, W. C. (2014). Item response theory models for performance decline during testing. Journal of Educational Measurement, 51, 178–200.

    Article  Google Scholar 

  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.

    PubMed  Article  Google Scholar 

  54. Alexeev, N., Templin, J., & Cohen, A. S. (2011). Spurious latent classes in the mixture Rasch model. Journal of Educational Measurement, 48, 313–332.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    PubMed  Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

Download references

Acknowledgements

Funding for this study was provided by the Canadian Institutes of Health Research (grant # MOP-142404). LML is supported by a Tier 1 Canada Research Chair in Methods for Electronic Health Data Quality. RS is supported by a Tier 2 Canada Research Chair in Patient-Reported Outcomes. BDZ is supported by the Tier 1 Canada Research Chair in Psychometrics and Measurement and the UBC-Paragon Research Initiative in support of his Paragon UBC Professor of Psychometrics and Measurement. The authors gratefully acknowledge the contributions of the University of Calgary Research Computing Services towards this study.

Funding

The work was supported by Canadian Institutes of Health Research Grant No # MOP-142404.

Author information

Authors and Affiliations

Authors

Contributions

All authors contributed to the study conception and design of the simulation study. DS and TS implemented the simulation study and summarized the results. All authors were involved in the critical revision of the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Tolulope T. Sajobi.

Ethics declarations

Conflict of interest

Dr. Sajobi has received consulting fees from Circle Neurovascular Imaging Inc. All other authors have no relevant financial or non-financial interests to disclose.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (DOCX 25 kb)

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Sajobi, T.T., Lix, L.M., Russell, L. et al. Accuracy of mixture item response theory models for identifying sample heterogeneity in patient-reported outcomes: a simulation study. Qual Life Res (2022). https://doi.org/10.1007/s11136-022-03169-0

Download citation

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11136-022-03169-0

Keywords

  • Patient-reported outcomes measures
  • Latent class
  • Item response theory
  • Measurement invariance
  • Computer simulation