Advances in Geospatial Survival Modeling for Small Area Cancer Data

描述（由申请人提供）：摘要该目标的主要焦点是扩大幸存者、密度和危险函数的定义，以包括通过明确的空间标记 空间依赖性建模。这涉及（s，t）、S（s，t）和h（s，t）及其相关边际和条件函数的直接推导。将检查这些新颖推导与标准地理增强生存分布的应用。空间依赖审查也是一个子目标的焦点。我们计划对此方面进行建模，并评估其在直接空间和上下文生存模型中的作用。生存建模中的预测变量可以是个体（年龄，性别，种族）它们与生存风险的联系也可能在空间上有所不同，其中预测变量选择具有空间标签，并且某些区域确实在模型中包含和排除其他预测变量。我们计划通过使用贝叶斯范式来实施上述建模方法，并且可能会使用基于 McMC 的软件包，或者在适当的情况下使用 INLA。 BRugs、R2WinBUGS、R2OpenBUGS）用于此目的。

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.