STATISTICAL METHODS FOR DISEASE PREVENTION TRIALS

疾病预防试验的统计方法

• 批准号: 6102661
• 负责人: Ross L Prentice
• 金额: $ 18.91万
• 依托单位: FRED HUTCHINSON CANCER RESEARCH CENTER
• 依托单位国家: 美国
• 财政年份: 1999
• 资助国家: 美国
• 起止时间: 1999-02-11 至 1999-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6102661
• 关键词: cancer prevention; human data; method development; statistics /biometry

This continuing project aims to develop and evaluate improved methods for the design, conduct and analysis of Disease Prevention and Risk Factor Intervention Trials. A continuing major emphasis will be placed on the development and evaluation of methods for the analysis of correlated failure time data as may arise in prevention trials having multiple disease outcomes, or in risk factor intervention trials with behavioral or disease outcomes by virtue of group randomization. Estimating equations for marginal hazard ratio parameters and for cumulative hazard correlation parameters will be adapted to apply to common baseline hazard models, and estimation procedures for parameters in semiparametric marginal hazard function models and semiparametric covariance rate models will be developed and evaluated. Summary indices that may be used as an aid to assessing benefits versus risks in prevention trial monitoring will be -proposed and studied, including composite univariate indices and multivariate indices. Various additional trial monitoring topics in multiarm and factorial prevention trials will also be considered, along with related estimation issues. Various topics in the analysis of community intervention trials will be addressed, including estimating equation, bootstrap and rank-based methods, with particular emphasis on group randomized trials in which the number of randomization units is fairly small. Methods for extracting additional information from prevention Intervention trials will also be identified and evaluated, primarily in the context of prevention trials in which the investigators have ongoing responsibilities. These include methods for explanatory analyses that aim to identify the elements of multi-faceted interventions responsible for any observed disease benefit or risk; methods for extrapolation of results beyond the specific randomized comparisons, based on the possibility of introducing clinical trial-based bias correction terms into the analysis of related observational data; and methods for the identification and use of auxiliary endpoint data to strengthen tests and estimates, and for the identification of surrogate endpoints to streamline subsequent trials of related interventions. Collectively, the proposed research has the potential to increase the efficiency and reduce the cost of prevention trials, and enhance the scientific and public health knowledge gained.

这个持续项目旨在开发和评估改进的方法 疾病预防和危险因素的设计、实施和分析 干预试验。 将继续重点关注发展和 相关故障时间数据分析方法的评估 可能出现在具有多种疾病结果的预防试验中，或者 在行为或疾病结果的危险因素干预试验中 凭借组随机化。边际估计方程 危险比参数和累积危险相关参数 将进行调整以适用于常见的基线危险模型和估计 半参数边际风险函数中的参数过程 将开发模型和半参数协方差率模型 评价。 可用于帮助评估效益与效益的汇总指数 将提出并研究预防试验监测中的风险， 包括综合单变量指数和多元指数。各种各样的 多臂和因素预防中的其他试验监测主题 还将考虑试验以及相关的估计问题。 社区干预试验分析中的各种主题将 解决的问题，包括估计方程、引导程序和基于等级的 方法，特别强调分组随机试验，其中 随机化单元的数量相当少。 从预防中提取附加信息的方法 还将确定和评估干预试验，主要是在 调查人员正在进行的预防试验的背景 责任。其中包括旨在 确定多方面干预措施的要素 任何观察到的疾病益处或风险；外推法 超出特定随机比较的结果，基于 引入基于临床试验的偏差校正术语的可能性 分析相关观测数据；和方法 识别和使用辅助终点数据以加强测试和 估计，并确定替代终点 简化相关干预措施的后续试验。 总的来说，拟议的研究有可能增加 效率并降低预防试验的成本，并增强 获得科学和公共卫生知识。