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， 疾病预防和危险因素的设计、实施和分析 干预试验。 一个持续的主要重点将放在发展和 相关失效时间数据分析方法的评价 如在具有多种疾病结局的预防试验中可能出现的，或 在行为或疾病结果的风险因素干预试验中 通过随机分组。边际收益的估计方程 风险比参数和累积风险相关参数 将进行调整，以适用于共同的基线危害模型， 半参数边际风险函数中参数的求法 将开发模型和半参数协方差率模型， 评估。 可用于帮助评估效益的总指数， 将提出和研究预防试验监测中的风险， 包括复合单变量指数和多变量指数。各种 多组和因子预防中的其他试验监测主题 试验也将被考虑，沿着相关的估计问题。 社区干预试验分析中的各种主题将 解决，包括估计方程，自助法和基于秩 方法，特别强调组随机试验，其中 随机化单元的数量相当小。 从预防中提取额外信息的方法 还将确定和评估干预试验，主要是在 在预防试验的背景下，研究人员正在进行 责任这些方法包括解释性分析， 确定多方面干预措施的要素， 任何观察到的疾病获益或风险;外推方法 结果超出了特定的随机比较，基于 引入基于临床试验的偏倚校正项的可能性 分析相关观测数据;以及 确定和使用辅助终点数据以加强测试， 估计值，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 降低预防试验的成本，并提高 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.