Frailty modeling for spatially correlated survival data, with application to infant mortality in Minnesota
Frailty modeling for spatially correlated survival data, with application to infant mortality in Minnesota
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DOI：
10.1093/biostatistics/4.1.123
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发表时间：
2003-01-01
期刊：
影响因子：
2.1
通讯作者：
Carlin, BP
中科院分区：
文献类型：
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作者：
Banerjee, S;Wall, MM;Carlin, BP
涉及随机效应或“脆弱”术语的生存模型的使用正变得越来越普遍。通常假设随机效应代表不同的集群，并且集群被假设为独立的。在本文中，我们考虑随机效应对应于集群的空间安排，如临床地点或地理区域。也就是说，我们可能会怀疑邻近地层对应的随机效应在量级上也可能相似。这种地层的空间排列可以用几种方式建模，但我们将这些方式分为两种一般设置：地质统计学方法，我们使用地层的精确地理位置（例如纬度和经度），以及晶格方法，我们只使用地层相对于彼此的位置（例如哪些县与哪些县相邻）。我们在1992年至1996年期间明尼苏达州各县婴儿死亡率数据集的背景下比较了我们的方法。我们的主要实质性目标是使用重要的协变量（性别、种族、出生体重、母亲年龄等）解释婴儿死亡率的模式，同时考虑到县之间可能的（空间相关的）危险差异。我们使用GIS ArcView来绘制结果拟合的危险率，以帮助搜索可能存在的空间相关性。DIC标准（Spiegelhalter et al., Journal of Royal Statistical Society, Series B 2002，即将出现）用于在各种相互竞争的模型中进行选择。我们研究了我们所选择的模型的拟合质量，并比较了用于调查新生儿和新生儿后期死亡率的结果。我们还比较了我们的时间到事件结果生存模型与更简单的二分类结果逻辑模型的使用。最后，对研究结果进行了总结，并对未来的研究方向提出了建议。
The use of survival models involving a random effect or 'frailty' term is becoming more common. Usually the random effects are assumed to represent different clusters, and clusters are assumed to be independent. In this paper, we consider random effects corresponding to clusters that are spatially arranged, such as clinical sites or geographical regions. That is, we might suspect that random effects corresponding to strata in closer proximity to each other might also be similar in magnitude. Such spatial arrangement of the strata can be modeled in several ways, but we group these ways into two general settings: geostatistical approaches, where we use the exact geographic locations (e.g. latitude and longitude) of the strata, and lattice approaches, where we use only the positions of the strata relative to each other (e.g. which counties neighbor which others). We compare our approaches in the context of a dataset on infant mortality in Minnesota counties between 1992 and 1996. Our main substantive goal here is to explain the pattern of infant mortality using important covariates (sex, race, birth weight, age of mother, etc.) while accounting for possible (spatially correlated) differences in hazard among the counties. We use the GIS ArcView to map resulting fitted hazard rates, to help search for possible lingering spatial correlation. The DIC criterion (Spiegelhalter et al., Journal of the Royal Statistical Society, Series B 2002, to appear) is used to choose among various competing models. We investigate the quality of fit of our chosen model, and compare its results when used to investigate neonatal versus post-neonatal mortality. We also compare use of our time-to-event outcome survival model with the simpler dichotomous outcome logistic model. Finally, we summarize our findings and suggest directions for future research.