Petersen Janne, Bandeen-Roche Karen, Budtz-Jørgensen Esben, Larsen Klaus Groes
COPENHAGEN UNIVERSITY HOSPITAL, HVIDOVRE.
Psychometrika. 2012 Apr 1;77(2):244-262. doi: 10.1007/s11336-012-9248-6.
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.
潜在类别回归模型将协变量与诸如精神疾病等潜在结构联系起来。尽管可以进行完全最大似然估计，但估计通常分三步进行：(i) 在不考虑协变量的情况下拟合潜在类别模型；(ii) 预测潜在类别得分；(iii) 将得分对协变量进行回归。我们提出了一种预测类别得分的新方法，与基于后验概率的方法不同，该方法在第三步中产生参数的一致估计量。此外，在模拟研究中，新方法仅表现出轻微的效率损失。最后，在一项关于活动/锻炼的分析中对新方法和基于后验概率的方法进行了比较。
