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B1025
Title: Lorelogram models for spatially clustered binary data Authors:  Manuela Cattelan - University of Padova (Italy) [presenting]
Cristiano Varin - Ca Foscari University of Venice (Italy)
Abstract: 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. A modification of the method of generalized estimating equations is discussed 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 a two-step approach that combines optimal estimating equations for the regression parameters and pairwise likelihood for the lorelogram parameters. The methodology is illustrated with an analysis of a data set on the prevalence of malaria in children in the Gambia that was described previously.