Advances in Geospatial Survival Modeling for Small Area Cancer Data

描述(由申请人提供)：本目标的主要焦点是扩大幸存者、密度和危险函数的定义，以包括通过显式的空间标注 空间依存性的建模。这涉及到(S，t)、S(S，t)和h(S，t)及其相关的边缘函数和条件函数的直接推导。这些新的派生与标准的地理扩大生存分布的应用将被检查。空间依赖的审查也是一个重点，作为一个子目标。我们计划对这一方面进行建模，并在直接的空间和背景生存模型中评估这一方面的作用。生存模型中的预测因素可以是个体的(年龄、性别、种族等)，也可以是背景的(例如，人口普查地区的人口统计数据)。它们与生存风险的联系也可能在空间上有所不同。我们建议检查模型的发展，其中预测者选择具有空间标签，以及某些地区在模型中包括和排除预测者。我们计划通过使用贝叶斯范式来实现上面的建模方法，并且可能会使用基于MCMC的包，或者，如果合适的话，使用INLA。评估将基于模拟，我们将使用R和相关的链接软件(MCMCpack、Brugs、R2WinBUGS、R2OpenBUGS)来实现这一目的。

DESCRIPTION (provided by applicant): Abstract It is the primary focus of this aim to broaden the definition of the survivor, density and hazard function to include spatial labeling by explicit modeling of the spatial dependency. This involves the direct derivation of (s,t), S(s,t), and h(s,t and their related marginal and conditional functions. The application of these novel derivations with standard geographically-augmented survival distributions will be examined. Spatially dependent censoring is also a focus as a sub- aim. We plan to model this aspect and evaluate the role of this in direct spatial and contextual survival models. Predictors in survival modeling can be individual (age, gender, race etc) or contextual (e. g. census tract demographics). They can also vary spatially in their linkage to survival risk. We propose to examine the development of models where predictor selection has a spatial label and where some regions do include and other exclude predictors in models. We plan to implement the modeling approaches above via the use of the Bayesian paradigm and will likely use McMC based packages or, if appropriate, INLA. Evaluation will be simulation based and we will use R and associated linked software (MCMCpack, BRugs, R2WinBUGS, R2OpenBUGS) for this purpose.