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Predicting Latent Class Scores for Subsequent Analysis

Published online by Cambridge University Press:  01 January 2025

Janne Petersen ,
Karen Bandeen-Roche ,
Esben Budtz-Jørgensen  and
Klaus Groes Larsen
Janne Petersen*
Affiliation:
Copenhagen University Hospital, Hvidovre
Karen Bandeen-Roche
Affiliation:
Johns Hopkins University
Esben Budtz-Jørgensen
Affiliation:
University of Copenhagen
Klaus Groes Larsen
Affiliation:
H. Lundbeck A/S
*
Requests for reprints should be sent to Janne Petersen, Copenhagen University Hospital, Hvidovre, Denmark. E-mail: petersen.janne@gmail.com
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Abstract

Latent class regression models relate covariates and latent constructs such as psychiatric disorders. Though full maximum likelihood estimation is available, estimation is often in three steps: (i) a latent class model is fitted without covariates; (ii) latent class scores are predicted; and (iii) the scores are regressed on covariates. We propose a new method for predicting class scores that, in contrast to posterior probability-based methods, yields consistent estimators of the parameters in the third step. Additionally, in simulation studies the new methodology exhibited only a minor loss of efficiency. Finally, the new and the posterior probability-based methods are compared in an analysis of mobility/exercise.

Keywords

classificationlatent class regressionlatent class scoresthree-step procedureleast squares class

Information

Type
Original Paper
Information
Psychometrika , Volume 77 , Issue 2 , April 2012 , pp. 244 - 262
Copyright
Copyright © 2012 The Psychometric Society

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References

Bandeen-Roche, K., Huang, G.H., Munoz, B., Rubin, G.S. (1999). Determination of risk factor associations with questionnaire outcomes: A methods case study. American Journal of Epidemiology, 150(11), 11651178CrossRefGoogle ScholarPubMed
Bandeen-Roche, K., Miglioretti, D.L., Zeger, S.L., Rathouz, P.J. (1997). Latent variable regression for multiple discrete outcomes. Journal of the American Statistical Association, 92, 1375–1386CrossRefGoogle Scholar
Bartolucci, F., Forcina, A. (2006). A class of latent marginal models for capture-recapture data with continuous covariates. Journal of the American Statistical Association, 101, 786–794CrossRefGoogle Scholar
Bolck, A., Croon, M., Hagenaars, J. (2004). Estimating latent structure models with categorical variables: One-step versus three-step estimators. Political Analysis, 12(1), 327CrossRefGoogle Scholar
Bollen, K.A. (1996). An alternative two stage least squares (2sls) estimator for latent variable equations. Psychometrika, 61, 109–121CrossRefGoogle Scholar
Carroll, R.J., Ruppert, D., Stefanski, L.A., Crainiceanu, C.M. (2006). Measurement error in nonlinear models: a modern perspective, Boca Raton: Chapman & Hall/CRCCrossRefGoogle Scholar
Croon, M. (2002). Using predicted latent scores in general latent structure models. In Marcoulides, G.A., Moustaki, I. (Eds.), Latent variable and latent structure models, Mahwah: Erlbaum 195225Google Scholar
Dayton, C.M., Macready, G.B. (1988). Concomitant-variable latent-class models. Journal of the American Statistical Association, 83, 173–178CrossRefGoogle Scholar
Funderbuck, J.S., Maisto, S.A., Sugarman, D.E., Wade, M. (2008). The covariation of multiple risk factors in primary care: a latent class analysis. Journal of Behavioral Medicine, 31, 525–535CrossRefGoogle Scholar
Goodman, L.A. (1974). Exploratory latent structure analysis using both identifiable and unidentifiable models. Biometrika, 61, 215–231CrossRefGoogle Scholar
Guralnik, J., Fried, L., Simonsick, E., Kasper, J., & Lafferty, M. (1995). The women’s health and aging study: health and social characteristics of older women with disability (Tech. Rep. No. NIH Pub. 95-4009). Bethesda MD: National Institute on Aging. Google Scholar
Huang, G.H., Bandeen-Roche, K. (2004). Building an identifiable latent class model with covariates effects on underlying and measured variables. Psychometrika, 69(1), 532CrossRefGoogle Scholar
Johnson, R.A., Wichern, D.W. (2007). Applied multivariate statistical analysis, New York: Pearson Prentice HallGoogle Scholar
Lazarsfeld, P.F., Henry, N.W. (1968). Latent structure analysis, Boston: Houghton MifflinGoogle Scholar
Liang, K.-Y., Zeger, S.L. (1986). Longitudinal data analysis using generalized linear models. Biometrika, 73, 13–22CrossRefGoogle Scholar
Lu, I.R.R., Thomas, R. (2008). Avoiding and correcting bias in score-based latent variable regression with discrete manifest items. Structural Equation Modeling, 15, 462–490CrossRefGoogle Scholar
Markkula, R., Jarvinen, P., Leino-Arjas, P., Koskenvuo, M., Kalso, E., Kaprio, J. (2009). Clustering of symptoms associated with fibromyalgia in a finnish twin cohort. The European Journal of Pain, 13(7), 744750CrossRefGoogle Scholar
Munoz, B., West, S., Rubin, G.S., Schein, O.D., Fried, L.P., Bandeen-Roche, K. et.al (1999). Who participates in population based studies of visual impairment? The Salisbury eye evaluation project experience. Annals of Epidemiology, 9(1), 5359CrossRefGoogle ScholarPubMed
Muthén, L.K., Muthén, B.O. (2007). Mplus, statistical analysis with latent variables, user’s guide, (5rd ed.). Los Angeles: Muthén & MuthénGoogle Scholar
Nylund, K.L., Asparouhov, T., Muthen, B.O. (2007). Deciding on the number of classes in latent class analysis and growth mixture modeling: A Monte Carlo simulation study. Structural Equation Modeling, 14, 535–569CrossRefGoogle Scholar
Pepe, M.S. (2003). The statistical evaluation of medical tests for classification and prediction, Oxford: Oxford University PressCrossRefGoogle Scholar
Reyes, A.D.L., Henry, D.B., Tolan, P.H., Wakschlag, L.S. (2009). Linking informant discrepancies to observed variations in young children’s disruptive behavior. Journal of Abnormal Child Psychology, 37(5), 637652CrossRefGoogle Scholar
Rubin, G.S., Bandeen-Roche, K., Prasada-Rao, P., Fried, L. (1994). Visual impairment and disability in older adults. Optometry & Vision Science, 71, 750–760CrossRefGoogle ScholarPubMed
Sánchez, B.N., Budtz-Jørgensen, E., Ryan, L.M. (2009). An estimating equations approach to fitting latent exposure models with longitudinal health outcomes. Annals of Applied Statistics, 3, 830–856CrossRefGoogle Scholar
Schorr, G., Ulbricht, S., Schmidt, C.O., Baumeister, S.E., Ruge, J., Schumann, A. et.al (2008). Does precontemplation represent a homogeneous stage category? A latent class analysis on German smokers. Journal of Consulting and Clinical Psychology, 76(5), 840851CrossRefGoogle ScholarPubMed
Schwarz, G. (1978). Estimating the dimension of a model. Annals of Statistics, 6, 461–464CrossRefGoogle Scholar
Serfling, R.J. (1980). Approximation theorems of mathematical statistics, New York: WileyCrossRefGoogle Scholar
Skrondal, A., Laake, P. (2001). Regression among factor scores. Psychometrika, 4, 563–576CrossRefGoogle Scholar
Steiger, H., Richardson, J., Schmitz, N., Joober, R., Israel, M., Bruce, K.R. et.al (2009). Association of trait-defined, eating-disorder sub-phenotypes with (biallelic and triallelic) 5httlpr variations. Journal of Psychiatric Research, 43(13), 10861094CrossRefGoogle ScholarPubMed
Unick, G.J., Snowden, L., Hastings, J. (2009). Heterogeneity in comorbidity between major depressive disorder and generalized anxiety disorder and its clinical consequences. The Journal of Nervous and Mental Disease, 197(4), 215224CrossRefGoogle ScholarPubMed
Wang, C.P., Brown, C.H., Bandeen-Roche, K. (2005). Residual diagnostics for growth mixture models: Examining the impact of a preventive intervention on multiple trajectories of aggressive behavior. Journal of the American Statistical Association, 100(471), 10541076CrossRefGoogle Scholar
West, C.G., Gildengorin, G., Haegerstrom-Portnoy, G., Schneck, M.E., Lett, L., Brabyn, J.A. (2002). Is vision function related to physical functional ability in older adults?. Journal of the American Geriatrics Society, 50, 136–145CrossRefGoogle ScholarPubMed
West, S.K., Munoz, B., Rubin, G.S., Schein, O.D., Bandeen-Roche, K., Zeger, S.L. et.al (1997). Function and visual impairment in a population-based study of older adults: The SEE project. Investigative Ophthalmology & Visual Science, 38(1), 7282Google Scholar