The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Latent Variable Regression for Multiple Discrete Outcomes
Abstract
Quantifying human health and functioning poses significant challenges in many research areas. Commonly in the social and behavioral sciences and increasingly in epidemiologic research, multiple indicators are utilized as responses in lieu of an obvious single measure for an outcome of interest. In this article we study the concomitant latent class model for analyzing such multivariate categorical outcome data. We develop practical theory for reducing and identifying such models. We detail parameter and standard error fitting that parallels standard latent class methodology, thus supplementing the approach proposed by Dayton and Macready. We propose and study diagnostic strategies, exemplifying our methods using physical disability data from an ongoing gerontologic study. Throughout, the focus of our work is on applications for which a primary goal is to study the association between health or functioning and covariates.
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Citations
Cites
Maximum Likelihood from Incomplete Data Via the EM Algorithm
Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1977.tb01600.x · Cited by 50,444
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Cited by
poLCA: An R Package for Polytomous Variable Latent Class Analysis
Linzer, D. A., & Lewis, J. B. (2011) · Journal of Statistical Software · doi:10.18637/jss.v042.i10 · Cited by 1,636
Beyond SEM: General latent variable modeling
Muthén, B. O. (2002) · Behaviormetrika · doi:10.2333/bhmk.29.81 · Cited by 1,074
Bakk, Z., Tekle, F. B., & Vermunt, J. K. (2013) · Sociological Methodology · doi:10.1177/0081175012470644 · Cited by 629
Fried, L. P., Xue, Q.-L., Cappola, A. R., Ferrucci, L., Chaves, P., Varadhan, R., Guralnik, J. M., Leng, S. X., Semba, R. D., Walston, J. D., et al. (2009) · The Journals of Gerontology Series A: Biological Sciences and Medical Sciences · doi:10.1093/gerona/glp076 · Cited by 539
Self-citationBrown, C. H., Wang, W., Kellam, S. G., Muthén, B. O., Petras, H., Toyinbo, P., Poduska, J., Ialongo, N., Wyman, P. A., Chamberlain, P., et al. (2008) · Drug and Alcohol Dependence · doi:10.1016/j.drugalcdep.2007.11.013 · Cited by 177
Metadata
- Authors
- Bandeen-roche, K., Miglioretti, D. L., Zeger, S. L., & Rathouz, P. J.
- Venue
- Journal of the American Statistical Association
- Publisher
- Informa UK Limited
- Published
- 1997-12
- Volume
- 92
- Issue
- 440
- Pages
- 1375-1386
- ISSN
- 0162-1459 (ISSN-L)
- 1537-274X
- OpenAlex
- W2064452544
- Cited by
- 453
- References
- 4
- FWCI
- 6.42
- Type
- journal article
- Language
- en
- Open access
- closed
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- 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. https://doi.org/10.1080/01621459.1997.10473658
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