title: PREDICTING LATENT CLASS SCORES FOR SUBSEQUENT ANALYSIS.
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.
authors: COPENHAGEN UNIVERSITY HOSPITAL, HVIDOVRE.
authors: Janne
authors: J
authors: Petersen
authors: Karen
authors: K
authors: Bandeen-Roche
authors: Esben
authors: E
authors: Budtz-Jørgensen
authors: Klaus Groes
authors: KG
authors: Larsen
keywords: classification; latent class regression; latent class scores; least squares class; three-step procedure
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