Latent Variable Modeling of Diagnostic Accuracy |
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| 文献名称 | Latent Variable Modeling of Diagnostic Accuracy | ||||||
| 文献作者 | Ilsoon Yang and Mark P. Becker | ||||||
| 作者所在单位 | Department of Biostatistics, Harvard School of Public Health;Department of Biostatistics, School of Public Health, University of Michigan | ||||||
| 文献分类 | 已发表文献 | ||||||
| 学科一级分类 | 统计 | ||||||
| 学科二级分类 | 统计学 | ||||||
| 文献摘要 |
Latent class analysis has been applied in medical research to assessing the sensitivity and specificity of diagnostic tests/diagnosticians. In these applications, a dichotomous latent variable corresponding to the unobserved true disease status of the patients is assumed. Associations among multiple diagnostic tests are attributed to the unobserved heterogeneity induced by the latent variable, and inferences for the sensitivities and specificities of the diagnostic tests are made possible even though the true disease status is unknown. However, a shortcoming of this approach to analyses of diagnostic tests is that the standard assumption of conditional independence among the diagnostic tests given a latent class is contraindicated by the data in some applications. In the present paper, models incorporating dependence among the diagnostic tests given a latent class are proposed. The models are parameterized so that the sensitivities and specificities of the diagnostic tests are simple functions of model parameters, and the usual latent class model obtains as a special case. Marginal models are used to account for the dependencies within each latent class. An accelerated EM gradient algorithm is demonstrated to obtain maximum likelihood estimates of the parameters of interest, as well as estimates of the precision of the estimates. |
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| 参考文献 |
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| 关键字 | EM algorithm; HIV; Log-linear model; Marginal model; Sensitivity; Specificity | ||||||
| 发表所在刊物（或来源） | Biometrics, Vol. 53, No. 3 (Sep., 1997), pp. 948-958 | ||||||
| 发表时间 | Sep., 1997 | ||||||
| 适用研究领域 | 统计学 | ||||||
| 评论 | |||||||
| 上传时间 | 2011-1-20 09:29 | ||||||
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