STATISTICAL METHODS FOR CLINICAL STUDIES

描述（由申请人提供）： 降低癌症死亡率和发病率的干预措施是公众关注的关键。 兴趣应对新的和持续存在的挑战的实用工具 将制定临床研究的设计和分析。 重点将放在1-111期临床试验的设计上。 新的发展将包括新的早期临床研究的战略， 生物制剂，单臂生存研究的设计， 一些以前未解决的三期设计问题。灵活的统计 并将开发设计软件。 纵向和时间联合分析的新统计方法 将研究III期研究背景下的事件数据。的 方法将采用贝叶斯方法，并利用马尔可夫链蒙特卡罗 （MCMC）采样算法。 将开发和评价探索性生存分析 方法.构建和解释预后亚组的新算法 患者将被考虑。模型选择方法和 中等维度临床关联研究中的合并协变量 也将被调查。 提出的其他主题直接来自我们的临床合作工作， 审判它们将包括对阳性疾病状态的时间分析， 非参数协变量调整方法。 总的来说，该项目将有助于改进评价工作， 癌症治疗的有效性，通过更好的方法来设计， 临床研究分析。

DESCRIPTION (provided by applicant): The identification and evaluation of interventions to reduce mortality and incidence of cancer is of critical public interest. Practical tools for addressing some new and continuing challenges in the design and analysis of clinical studies will be developed. A major emphasis will be placed on the design for Phase 1-111 clinical trials. The new developments will include strategies for early clinical studies of new biologic agents, designs for single arm survival studies, and solutions to several previously unresolved Phase III design issues. Flexible statistical design software will also be developed. New statistical methods for the joint analysis of longitudinal and time to event data in the context of Phase III studies will be investigated. The methods will take a Bayesian approach and utilize Markov Chain Monte Carlo (MCMC) sampling algorithms. There will be development and evaluation of exploratory survival analysis methods. New algorithms for constructing and interpreting prognostic subgroups of patients will be considered. Methodologies for model selection and for combining covariates in clinical association studies of moderate dimensions will also be investigated. Other topics proposed arise directly from our collaborative work on clinical trials. They will include analysis for time within a positive disease state and methods for non-parametric covariate adjustment. Collectively, the project will contribute to improvements in evaluating efficacy of cancer therapies though better methods for design, conduct and analysis of clinical studies.