Kostoulas Polychronis, Leontides Leonidas, Browne William J, Gardner Ian A
Laboratory of Epidemiology, Biostatistics and Animal Health Economics, University of Thessaly, 224 Trikalon st., GR-43100 Karditsa, Greece.
Prev Vet Med. 2009 Jun 1;89(3-4):155-62. doi: 10.1016/j.prevetmed.2009.02.008. Epub 2009 Mar 17.
The variance partition coefficient (VPC) measures the clustering of infection/disease among individuals with a specific covariate pattern. Covariate-pattern-specific VPCs provide insight to the groups of individuals that exhibit great heterogeneity and should be targeted for intervention. VPCs should be taken into consideration when planning study designs, modeling data and estimating sample sizes. We present a Bayesian discrete mixed model for the estimation of covariate-pattern-specific VPCs when measurement of the infection/disease is based on an imperfect test. The utility of the presented model is demonstrated with three applications. In all cases, imperfect tests biased VPC estimates towards the null but corrected estimates could be obtained by modeling the sensitivity and specificity of the test procedure with beta distributions. The comparison of adjusted VPCs between the intercept only and the fitted models with higher level covariates explained the portion of heterogeneity in the data that was accounted for by the covariates.
方差划分系数（VPC）用于衡量具有特定协变量模式的个体之间感染/疾病的聚集情况。特定协变量模式的VPC有助于深入了解表现出高度异质性且应作为干预目标的个体群体。在规划研究设计、对数据进行建模以及估计样本量时，应考虑VPC。当感染/疾病的测量基于不完美检测时，我们提出了一种贝叶斯离散混合模型来估计特定协变量模式的VPC。通过三个应用实例展示了所提出模型的实用性。在所有情况下，不完美检测会使VPC估计偏向无效值，但通过用贝塔分布对检测程序的灵敏度和特异性进行建模，可以获得校正后的估计值。仅截距模型与包含更高层次协变量的拟合模型之间调整后的VPC比较，解释了数据中由协变量所解释的异质性部分。
