Advances in Geospatial Survival Modeling for Small Area Cancer Data

小区域癌症数据地理空间生存建模的进展

• 批准号: 8705126
• 负责人: Andrew B. Lawson
• 金额: $ 7.31万
• 依托单位: MEDICAL UNIVERSITY OF SOUTH CAROLINA
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-04-01 至 2016-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8705126
• 关键词: Address; Advocate; Affect; Age; Area; Behavior; Cancer Model; Censuses; Computer software; County; Data; Dependence; Dependency; Derivation procedure; Development; Diagnosis; Disease; Disease remission; Environmental Risk Factor; Evaluation; Event; Gender; Geographic Locations; Grouping; Health Resources; Individual; Label; Link; Location; Malignant Neoplasms; Measures; Methodology; Methods; Modeling; Outcome; Persons; Probability; Race; Recovery; Relapse; Research Personnel; Risk; Role; Survival Analysis; Survivors; Time; Variant; Weight; abstracting; base; cancer diagnosis; cancer risk; cancer type; demographics; density; experience; geographic difference; hazard; model development; novel; novel strategies; pollutant; public health relevance; residence; simulation; waste treatment

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.

描述（由申请人提供）：摘要该目标的主要焦点是通过显式的空间标记来扩展幸存者、密度和危险函数的定义，以包括空间标记。 空间依赖性模型。这涉及到（s，t）、S（s，t）和h（s，t）的直接推导以及它们相关的边际函数和条件函数。这些新的衍生标准的地理增强生存分布的应用将进行检查。空间相关删失也是一个研究热点.我们计划对这方面进行建模，并评估其在直接空间和环境生存模型中的作用。生存建模中的预测因子可以是个体的（年龄，性别，种族等）或背景的（例如。G.人口普查区人口统计数据）。它们在与生存风险的联系方面也可能存在空间差异。我们建议研究模型的发展，其中预测因子选择具有空间标签，并且某些区域确实包括模型中的预测因子，而其他区域则排除模型中的预测因子。我们计划通过使用贝叶斯范式来实现上述建模方法，并可能使用基于McMC的软件包，或者在适当的情况下使用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）。