Combining data from multiple spatially referenced prevalence surveys using generalized linear geostatistical models
Combining data from multiple spatially referenced prevalence surveys using generalized linear geostatistical models
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DOI：
10.1111/rssa.12069
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发表时间：
2015-02-01
影响因子：
2
通讯作者：
Diggle, Peter J.
中科院分区：
文献类型：
--
作者：
Giorgi, Emanuele;Sesay, Sanie S. S.;Diggle, Peter J.
来自多个流行率调查的数据可以提供有关感兴趣的共同参数的信息，因此，在联合分析中可以比通过单独分析每个调查的数据更准确地估计这些参数。然而，在没有检验所有调查都针对同一推论目标这一隐含假设的情况下，将单一模型与来自多个调查的组合数据进行拟合是不可取的。我们提出了一个多元广义线性地统计学模型，该模型考虑了调查中的两个异质性来源，以纠正非随机化调查中的空间结构偏差，并考虑到连续调查期间潜在流行表面的时间变化。我们描述了一种用于参数估计的蒙特卡罗最大似然方法，并通过模拟实验说明了如何在联合模型中考虑调查之间的不同异质性来源导致更准确的推断。我们描述了在马拉维南部Chikhwawa区进行的疟疾流行的多项调查的应用，并讨论了这种方法如何为混合抽样战略提供参考，该战略结合了随机和非随机调查的数据，以最有效地利用所有可用的数据。
Data from multiple prevalence surveys can provide information on common parameters of interest, which can therefore be estimated more precisely in a joint analysis than by separate analyses of the data from each survey. However, fitting a single model to the combined data from multiple surveys is inadvisable without testing the implicit assumption that all of the surveys are directed at the same inferential target. We propose a multivariate generalized linear geostatistical model that accommodates two sources of heterogeneity across surveys to correct for spatially structured bias in non-randomized surveys and to allow for temporal variation in the underlying prevalence surface between consecutive survey periods. We describe a Monte Carlo maximum likelihood procedure for parameter estimation and show through simulation experiments how accounting for the different sources of heterogeneity among surveys in a joint model leads to more precise inferences. We describe an application to multiple surveys of the prevalence of malaria conducted in Chikhwawa District, Southern Malawi, and discuss how this approach could inform hybrid sampling strategies that combine data from randomized and non-randomized surveys to make the most efficient use of all available data.