EPIDEMIOLOGIC METHODS FOR CASE-CONTROL/ECOLOGIC STUDIES

该项目的主要目的是研究一些 与流行病学分析有关的统计问题 数据 由于优势比构成了许多流行病学的核心， 分析，本研究解决了建筑建模中的问题， 优势比，更一般地，相对风险或对数线性 建模 研究对比了选定的统计估计 程序. 计算机和理论技术用于 制定和比较方法。 此外该 将方法应用于选定的癌症病例对照数据集， 这些数据集被用于比较， 评价所制定的方法。 除了与比值比相关的方法外， 提出了生存数据分析的主题。 流行病学 数据通常包含一个代表时间的结果变量 特定事件，如癌症的发展。 在本研究中， 与生存数据分析相关的某些问题 将通过分析和 应用技术。 在可能的情况下，现有病例的数据- 控制和SEER注册在爱荷华州大学将是 在应用和比较中使用。 这项研究是前两年的延续， 研究中的一些统计问题， 研究了 这些问题与处理方法有关 稀疏多维数据 这样的数据在 流行病学研究和改进的分析方法， needed. 这项研究将导致改进统计方法， 分析复杂的流行病学数据。 研究的问题 对这些数据的分析有直接的影响。

The principal aims of this project are to study a number of statistical problems associated with the analysis of epidemiologic data. Since odds ratios form the nucleus of many epidemiologic analyses, this research addresses issues in modeling building with odds ratios and more generally with relative risk or log-linear modeling. The research contrasts selected statistical estimation procedures. Computer and theoretical techniques are used to develop and compare the methodology. In addition, the methodology is applied to selected cancer case-control data sets, and these data sets are utilized in the comparisons and evaluations of the methods developed. In addition to methods associated with odds ratios, this grant proposes topics in analysis of survivorship data. Epidemiologic data often involve an outcome variable which represents the time to a specified event such as development of cancer. In this study, certain problems associated with the analysis of survivorship data are addressed and will be carefully studied through analytical and applied techniques. Where possible, data from existing case- control and the SEER registry at the University of Iowa will be utilized in the applications and comparisons. This research is a continuation of the previous two years of research in which a number of statistical problems have been studied. These problems are associated with methods for handling sparse multidimensional data. Such data are typical in epidemiological research and improved methods of analysis are needed. This research will lead to improved statistical methodology for the analysis of complex epidemiologic data. The issues studied have immediate implications for the analysis of such data.