MODIFIED SCORE METHODS FOR CANCER CASE-CONTROL STUDIES

癌症病例对照研究的改良评分方法

• 批准号: 3446851
• 负责人: JAMES B KAMPERT
• 金额: $ 6.19万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 1986
• 资助国家: 美国
• 起止时间: 1986-09-15 至 1989-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/3446851
• 关键词: breast neoplasms; cancer information system; cancer risk; computer simulation; mathematical model; neoplasm /cancer classification /staging

The proposed work will develop, evaluate, and apply new statistical methodology to analyze case-control data. The new methods are designed to efficiently and inexpensively combine multivariate data from numerous large strata in fitting general relative risk models. These methods appreciably curtail high computing costs in using large-scale (mainframe) computers, compared to existing methods. They significantly improve slow computing times in using smaller time-sharing minicomputers or microcomputers with limited addressable memory. The specific aims of the proposed work are: 1) to extend the newly developed method of modified-score logistic risk analysis to more general relative risk functions; 2) to develop and test improved estimators to reduce small-strata estimation bias and speed convergence to asymptotic behavior; 3) to develop methods to efficiently combine multivariate data from numerous large and small strate, with minimal bias and computational effort; 4) to develop analysis of residuals and measures of deviance for fitting nested models and indicating model departures; 50 to develop regression diagnostics for detecting outliers and data points with undue influence on parameter estimates; 6) to develop methods for estimating multivariate attributable risk in matched or stratified case-control data; and 7) to develop methods for classification and discrimination in these study designs. Monte-Carlo sampling experiments (computer simulations) using a variety of case-control study designs will be conducted to demonstrate the performance of the new methods in comparison to alternative methods. The new methodology will be used in fitting proportional hazards models to existing data for 1884 breast cancer patients and 3432 control patients in multivariate prediction of breast cancer risk. Attributable risk of breast cancer due to single and multiple factors in the presence of all others will be estimated. Multifactorial classification of women at high and low short-term and lifetime risk will be determined. Additivity and multiplicativity of risk predictors will be investigated. A portable, control-statement operated computer program to implement the new methodology will be developed and made available to others.

拟议的工作将开发、评估和应用新的统计数据 分析病例对照数据的方法。 新方法旨在 高效且廉价地组合来自大量大型数据的多变量数据 拟合一般相对风险模型时的分层。 这些方法明显 减少使用大型（大型机）计算机的高计算成本， 与现有方法相比。 它们显着改善了缓慢的计算 使用较小的分时小型机或微型计算机的时间 可寻址内存有限。 拟议工作的具体目标是： 1）扩展了新开发的修正评分逻辑风险方法 分析更一般的相对风险函数； 2）开发和测试 改进了估计器，以减少小层估计偏差和速度 收敛到渐近行为； 3）制定有效的方法 结合来自大量大大小小的策略的多元数据， 最小的偏差和计算量； 4) 开展残差分析 以及拟合嵌套模型和指示模型的偏差测量 出港; 50 开发回归诊断来检测异常值和 对参数估计有不当影响的数据点； 6）开发 估计匹配或匹配的多变量归因风险的方法 分层病例对照数据； 7) 开发分类方法 这些研究设计中的歧视。 使用各种方法进行蒙特卡洛抽样实验（计算机模拟） 将进行病例对照研究设计以证明其性能 新方法与替代方法的比较。 新的 方法将用于将比例风险模型拟合到现有的 1884 名乳腺癌患者和 3432 名对照患者的数据 乳腺癌风险的多变量预测。 乳房的归因风险 在所有其他因素存在的情况下，由单一因素和多种因素导致的癌症 将被估计。 女性高低的多因素分类 短期和终生风险将被确定。 可加性和 将研究风险预测因子的多重性。 一个便携式、 控制语句操作的计算机程序来实现新的 将制定方法并提供给其他人。