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: 8828611
• 负责人: Andrew B. Lawson
• 金额: $ 7.29万
• 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/8828611
• 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; 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; geographic risk; hazard; model development; novel; novel strategies; pollutant; 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) 的直接推导及其相关的边际函数和条件函数。将检查这些新颖推导与标准地理增强生存分布的应用。空间相关审查也是一个子目标，我们计划对这一方面进行建模，并评估其在直接空间和情境生存模型中的作用。生存建模中的预测因素可以是个体（年龄、性别、种族）。等）或上下文（例如人口普查区人口统计），它们与生存风险的联系也可能在空间上有所不同，我们建议研究预测变量选择具有空间标签以及某些区域确实在模型中包含和排除预测变量的模型的开发。我们计划通过使用贝叶斯范式来实现上述建模方法，并且可能会使用基于 McMC 的软件包，或者如果合适的话，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）用于此目的。

项目成果

Bayesian cure-rate survival model with spatially structured censoring.

具有空间结构审查的贝叶斯治愈率生存模型。

• DOI: 10.1016/j.spasta.2018.08.007
• 发表时间: 2018
• 期刊: Spatial statistics
• 影响因子: 2.3
• 作者: Onicescu,Georgiana;Lawson,AndrewB
• 通讯作者: Lawson,AndrewB

Bayesian accelerated failure time model for space-time dependency in a geographically augmented survival model.

• DOI: 10.1177/0962280215596186
• 发表时间: 2017-10
• 期刊: Statistical methods in medical research
• 影响因子: 2.3
• 作者: Onicescu G;Lawson A;Zhang J;Gebregziabher M;Wallace K;Eberth JM
• 通讯作者: Eberth JM

Spatially explicit survival modeling for small area cancer data.

小区域癌症数据的空间明确生存模型。

• DOI: 10.1080/02664763.2017.1288200
• 发表时间: 2018
• 期刊: Journal of applied statistics
• 影响因子: 1.5
• 作者: Onicescu,G;Lawson,A;Zhang,J;Gebregziabher,Mulugeta;Wallace,Kristin;Eberth,JM
• 通讯作者: Eberth,JM

Spatially-explicit survival modeling with discrete grouping of cancer predictors.

具有离散分组的癌症预测因子的空间明确的生存模型。

• DOI: 10.1016/j.sste.2018.06.001
• 发表时间: 2019
• 期刊: Spatial and spatio-temporal epidemiology
• 影响因子: 3.4
• 作者: Onicescu,Georgiana;Lawson,AndrewB;Zhang,Jiajia;Gebregziabher,Mulugeta;Wallace,Kristin;Eberth,JanM
• 通讯作者: Eberth,JanM