RESEARCH IN BIOSTATISTICAL METHODOLOGY AND MATHEMATICAL MODELING

这个项目的目的是发展生物统计学方法。 适用于与以下疾病相关的流行病学和实验研究 癌症防控。 泊松回归方法常被用来分析两者之间的关系 一个县的癌症死亡率与其人口、经济和生态 特点。由于癌症风险在成员中的变异性 该县的人口，泊松假设在理论上是无效的。 本研究将评估县域内时间的实际程度 主要癌症部位的异质性。 应用COX回归技术时应使用的适当时间度量 为了分析队列中一段时间内的癌症发病率， 模拟方法。数据是根据已知的癌症生成的 所有原因的特定年龄发病率和已知死亡率。时间 正在研究的指标是从研究开始的随访时间和年龄 风险。不同的学习时间也在评估中。此外, 正确估计风险因素影响的不同方法 与确定的年龄有关的因素正在评估中。 在分析时，通常会提出“比例风险”的假设。 癌症治疗患者的存活率。只有中的Scale参数 参数模型与半参数中的比例参数 假设模型因患者的预后而有所不同。 变量。因为外来的危险率异质性可能会产生 与这一假设的实质性偏离，这项研究将比较 威布尔模型与伽玛混合威布尔模型的拟合 估计异质性程度及其对估计的偏差影响 参数和危险比。

The purpose of this project is the development of biostatistical methods appropriate for epidemiological and experimental studies related to cancer prevention and control. Poisson regression methods are often used to analyze the relationship of a county's cancer mortality to its demographic, economic, and ecologic characteristics. Because of variability of cancer risk among members of the county's population, the Poisson assumption is theoretically invalid. This research will evaluate the actual degree of intra-county temporal heterogeneity for the major cancer sites. The proper time metric to be used when applying Cox regression techniques to analyze a cohort's cancer incidence over time is being studied by simulation methods. Data are being generated according to known cancer age-specific incidence and known mortality from all causes. The time metrics being studied are follow-up time from study initiation and age at risk. Different study durations are also being evaluated. In addition, different methods to properly estimate the effect of risk factors which are related to age at ascertainment are being evaluated. The assumption of "proportional hazards" is commonly made when analyzing survival in patients treated for cancer. Only the scale parameter in parametric models and the proportionality parameter in semi-parametric models are assumed to vary among patients through their prognostic variables. Because extraneous hazard rate heterogeneity can produce substantial deviations from this assumption, this research will compare the fits of a Weibull model with a Gamma-mixture of Weibull models to estimate the degree of heterogeneity and its biasing effect on estimated parameters and on the hazard ratio.