Latent class model diagnosis
Latent class model diagnosis
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DOI：
10.1111/j.0006-341x.2000.01055.x
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发表时间：
2000-12-01
期刊：
影响因子：
1.9
通讯作者：
Zeger, SL
中科院分区：
文献类型：
--
作者：
Garrett, ES;Zeger, SL
在医学研究的许多领域，如精神病学和老年学，潜在的类变量用于将个体分类到疾病类别，通常具有分层建模的意图。当不清楚有多少疾病类别是合适的时，就会出现问题，从而需要模型选择和诊断技术。以前的工作表明，皮尔逊卡方检验（2）统计量和对数似然比G（2）统计量不是评估潜在类模型的有效检验统计量。其他方法，如信息标准，提供决策规则，而不提供有关模型和数据之间差异发生的显式信息。可识别性问题使这些问题进一步复杂化。本文开发的程序评估马尔可夫链蒙特卡罗收敛和模型诊断和选择的潜在变量的类别数的基础上，使用马尔可夫链蒙特卡罗技术的数据中的证据。模拟和精神病的例子来证明这些方法的有效使用。
In many areas of medical research, such as psychiatry and gerontology, latent class variables are used to classify individuals into disease categories, often with the intention of hierarchical modeling. Problems arise when it is not clear how many disease classes are appropriate, creating a need for model selection and diagnostic techniques. Previous work has shown that the Pearson chi (2) statistic and the log-likelihood ratio G(2) statistic are not valid test statistics for evaluating latent class models. Other methods, such as information criteria, provide decision rules without providing explicit information about where discrepancies occur between a model and the data. Identifiability issues further complicate these problems. This paper develops procedures for assessing Markov chain Monte Carlo convergence and model diagnosis and for selecting the number of categories for the latent variable based on evidence in the data using Markov chain Monte Carlo techniques. Simulations and a psychiatric example are presented to demonstrate the effective use of these methods.