STATISTICAL METHODS FOR DISEASE PREVENTION TRIALS

这个持续的项目旨在开发和评估改进的方法，以 疾病预防和危险因素的设计、实施和分析 干预试验。 将继续把主要重点放在发展和 相关故障时间数据分析方法的评价 在具有多种疾病结果的预防试验中可能出现的情况，或者 在有行为或疾病结果的危险因素干预试验中 通过分组随机化。关于边际的估计方程 危险比参数和累积危险关联参数 将适用于共同的基线风险模型，并进行估计 半参数边际风险函数中参数的计算方法 将开发模型和半参数协方差率模型，并 已评估。 可用来帮助评估收益与 预防试验监测中的风险将被提出和研究， 包括综合单变量指数和多变量指数。五花八门 多臂和因子预防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是 已解决，包括估计方程、自举和基于排名 方法，特别强调分组随机试验，在这些试验中 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。其中包括旨在实现以下目的的解释性分析方法 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；外推的方法 超出特定随机比较的结果，基于 引入以临床试验为基础的偏差校正术语的可能性 对相关观测数据的分析；以及 确定和使用辅助终点数据，以加强测试和 估计，并用于标识代理端点以 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 提高效率和降低预防试验的成本，并加强 获得科学和公共卫生知识。

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.