Abstract:
:Multiple outcomes are often used to properly characterize an effect of interest. This paper proposes a latent variable model for the situation where repeated measures over time are obtained on each outcome. These outcomes are assumed to measure an underlying quantity of main interest from different perspectives. We relate the observed outcomes using regression models to a latent variable, which is then modeled as a function of covariates by a separate regression model. Random effects are used to model the correlation due to repeated measures of the observed outcomes and the latent variable. An EM algorithm is developed to obtain maximum likelihood estimates of model parameters. Unit-specific predictions of the latent variables are also calculated. This method is illustrated using data from a national panel study on changes in methadone treatment practices.
journal_name
Biometricsjournal_title
Biometricsauthors
Roy J,Lin Xdoi
10.1111/j.0006-341x.2000.01047.xsubject
Has Abstractpub_date
2000-12-01 00:00:00pages
1047-54issue
4eissn
0006-341Xissn
1541-0420journal_volume
56pub_type
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