A CONDITIONAL APPROACH TO POINT PROCESS MODELING OF ELEVATED RISK
A CONDITIONAL APPROACH TO POINT PROCESS MODELING OF ELEVATED RISK
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DOI：
10.2307/2983529
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发表时间：
1994-01-01
影响因子：
2
通讯作者：
ROWLINGSON, BS
中科院分区：
文献类型：
--
作者：
DIGGLE, PJ;ROWLINGSON, BS
我们考虑调查与可能的环境因素相关的特定疾病的风险升高的问题。我们的出发点是迪格尔提出的一个非齐次泊松点过程模型，用于描述指定地理区域中病例和对照发生率的空间变化。我们开发了一种条件推理方法，将点过程模型转换为风险空间变化的非线性二元回归模型。模拟表明，基于似然的推理的常用渐近近似在这种条件设置中比在原始点过程设置中更可靠。我们提出了对与三个工业地点相关的哮喘空间分布的一些数据的应用。
We consider the problem of investigating the elevation in risk for a specified disease in relation to possible environmental factors. Our starting point is an inhomogeneous Poisson point process model for the spatial variation in the incidence of cases and controls in a designated geographic region, as proposed by Diggle. We develop a conditional approach to inference which converts the point process model to a non-linear binary regression model for the spatial variation in risk. Simulations suggest that the usual asymptotic approximations for likelihood-based inference are more reliable in this conditional setting than in the original point process setting. We present an application to some data on the spatial distribution of asthma in relation to three industrial locations.