Diggle Peter J, Guan Yongtao, Hart Anthony C, Paize Fauzia, Stanton Michelle
School of Health and Medicine, Lancaster University, Lancaster, U.K. and Adjunct Professor, Department of Biostatistics, Johns Hopkins University School of Public Health, Baltimore.
J Am Stat Assoc. 2010;105(492):1394-1402. doi: 10.1198/jasa.2010.ap09323. Epub 2012 Jan 1.
We propose a novel alternative to case-control sampling for the estimation of individual-level risk in spatial epidemiology. Our approach uses weighted estimating equations to estimate regression parameters in the intensity function of an inhomogeneous spatial point process, when information on risk-factors is available at the individual level for cases, but only at a spatially aggregated level for the population at risk. We develop data-driven methods to select the weights used in the estimating equations and show through simulation that the choice of weights can have a major impact on efficiency of estimation. We develop a formal test to detect non-Poisson behavior in the underlying point process and assess the performance of the test using simulations of Poisson and Poisson cluster point processes. We apply our methods to data on the spatial distribution of childhood meningococcal disease cases in Merseyside, U.K. between 1981 and 2007.
我们提出了一种用于空间流行病学中个体水平风险估计的病例对照抽样新方法。当病例的风险因素信息可在个体层面获取，而处于风险中的人群的风险因素信息仅在空间聚集层面可用时，我们的方法使用加权估计方程来估计非齐次空间点过程强度函数中的回归参数。我们开发了数据驱动的方法来选择估计方程中使用的权重，并通过模拟表明权重的选择会对估计效率产生重大影响。我们开发了一种正式检验来检测基础点过程中的非泊松行为，并使用泊松和泊松聚类点过程的模拟来评估该检验的性能。我们将我们的方法应用于1981年至2007年英国默西塞德郡儿童脑膜炎球菌病病例的空间分布数据。
