Garrett E S, Zeger S L
Oncology Center, Division of Biostatistics, Johns Hopkins University School of Medicine, 550 North Broadway, Baltimore, Maryland 21205, USA.
Biometrics. 2000 Dec;56(4):1055-67. doi: 10.1111/j.0006-341x.2000.01055.x.
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 G2 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.
在医学研究的许多领域，如精神病学和老年医学中，潜在类别变量用于将个体分类到疾病类别中，通常旨在进行层次建模。当不清楚合适的疾病类别数量时，就会出现问题，这就需要模型选择和诊断技术。先前的研究表明，Pearson卡方统计量和对数似然比G2统计量不是评估潜在类别模型的有效检验统计量。其他方法，如信息准则，提供了决策规则，但没有提供关于模型与数据之间差异发生位置的明确信息。可识别性问题使这些问题更加复杂。本文开发了基于马尔可夫链蒙特卡罗技术，根据数据中的证据评估马尔可夫链蒙特卡罗收敛性和模型诊断，以及选择潜在变量类别的数量的程序。通过模拟和一个精神病学实例来证明这些方法的有效应用。
