Marginal Logistic Regression for Spatially Clustered Binary Data
针对聚类数据可能存在的空间依赖性，提出一种改进的广义估计方程方法，通过参数化建模聚类间距离与相关性的关系，并用混合成对似然法估计参数，适用于流行病学等领域的二元数据分析。
Summary Clustered data are often analysed under the assumption that observations from distinct clusters are independent. The assumption may not be correct when the clusters are associated with different locations within a study region, as, for example, in epidemiological studies involving subjects nested within larger units such as hospitals, districts or villages. In such cases, correct inferential conclusions critically depend on the amount of spatial dependence between locations. We develop a modification of the method of generalized estimating equations to detect and account for spatial dependence between clusters in logistic regression for binary data. The approach proposed is based on parametric modelling of the lorelogram as a function of the distance between clusters. Model parameters are estimated by the hybrid pairwise likelihood method that combines optimal estimating equations for the regression parameters and pairwise likelihood for the lorelogram parameters. The methodology is illustrated with an analysis of prevalence disease survey data.