Innovative Spatial Survival Models with Geographically Varying Coefficients

具有地理差异系数的创新空间生存模型

• 批准号: 8431341
• 负责人: Jiajia Zhang
• 金额: $ 6.82万
• 依托单位: UNIVERSITY OF SOUTH CAROLINA AT COLUMBIA
• 依托单位国家: 美国
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-02-17 至 2015-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8431341
• 关键词: Code; Computer software; Data; Data Analyses; Data Set; Environment; Epidemiology; Equilibrium; Failure; Geographic Locations; Individual; Judgment; Louisiana; Malignant Neoplasms; Malignant neoplasm of prostate; Methods; Modeling; National Cancer Institute; Pattern; Performance; Play; Public Health; Research; Research Personnel; Role; SEER Program; South Carolina; Testing; Time; Work; base; flexibility; hazard; innovation; neoplasm registry; population based; public health relevance; residence; simulation; software development; user friendly software

DESCRIPTION (provided by applicant): We will develop a general "extended hazard" spatial survival model to predict geographical effects and geo- graphically varying effects in cancer survival. The proposed model can include the proportional hazards spatial survival model and the accelerated failure time spatial survival model as its special cases. Furthermore, the new model can correctly identify the geographical effects and geographically varying effects in cancer survival. The performance of the proposed model will be evaluated by a comprehensive simulation study. To demonstrate the usage of the proposed model, we will apply the proposed method to analyze prostate cancer within Louisiana from the Surveillance, Epidemiology, and End Results program, and prostate cancer data set from South Carolina Central Cancer Registry (SCCCR). The software development will solve the computational issue in practice and will enable the practitioners and researchers apply the proposed method easily.

描述（由申请人提供）：我们将开发一个一般的“扩展危险”空间生存模型，以预测癌症存活中的地理效应和地理上的效果。所提出的模型可以包括比例危害空间生存模型和加速的故障时间空间生存模型作为特殊情况。此外，新模型可以正确识别癌症存活中的地理作用和地理上不同的影响。拟议模型的性能将通过一项综合模拟研究评估。为了证明所提出的模型的使用，我们将采用拟议的方法来分析路易斯安那州的前列腺癌，从监视，流行病学和最终结果计划以及来自南卡罗来纳州中央癌症注册表（SCCCR）的前列腺癌数据集。软件开发将在实践中解决计算问题，并使从业者和研究人员可以轻松地应用提出的方法。

项目成果

Accelerated hazards model based on parametric families generalized with Bernstein polynomials.

• DOI: 10.1111/biom.12104
• 发表时间: 2014-03
• 期刊: Biometrics
• 影响因子: 1.9
• 作者: Chen Y;Hanson T;Zhang J
• 通讯作者: Zhang J