GROUP SEQUENTIAL METHODS FOR LONGITUDINAL DATA

纵向数据的分组顺序方法

• 批准号: 6528197
• 负责人: Scott S Emerson
• 金额: $ 13.12万
• 依托单位: UNIVERSITY OF WASHINGTON
• 依托单位国家: 美国
• 财政年份: 2001
• 资助国家: 美国
• 起止时间: 2001-09-30 至 2004-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6528197
• 关键词: clinical trials; computer program /software; computer system design /evaluation; data collection methodology /evaluation; experimental designs; human data; longitudinal human study

It is now commonplace for interim analyses to be performed in clinical trials. Such monitoring of the clinical trial data is essential to ensure that volunteer patients participating in the trial are treated ethically. Furthermore, the use of group sequential stopping rules can greatly increase the efficiency with which new treatments are adopted. The goal of this research is to broaden the spectrum of studies in which interim analyses can be used, with specific emphasis placed on methods appropriate for clinical trials which involve outcome measures based on measurements made over time. Such longitudinal studies pose unique problems in group sequential monitoring due to the limited timespan covered by the data at the earliest analyses. This variation in length of follow-up over the course of the study can have major impact on the robustness of the analytic methods used for determining treatment efficacy. Specific areas of research to be conducted under this project include: 1) development of testing strategies for survival data that are robust to crossing hazards; 2) development of testing strategies for longitudinal nonsurvival data that are robust to nonlinear treatment effects over time; 3) development of Bayesian methods for clinical trial monitoring that exhibit robustness properties similar to those developed for frequentist analysis of survival and other longitudinal data; 4) generalization of methods developed for multiple endpoints in order to address longitudinal data issues; 5) generalization of flexible monitoring schemes to accommodate robust longitudinal methods; and 6) development of computer software to implement these strategies in a clinical trials module.

现在，在临床试验中进行中期分析已很常见。 对临床试验数据的这种监测对于确保参与试验的志愿者患者得到合乎道德的治疗至关重要。 此外，使用组顺序停止规则可以大大提高采用新治疗方法的效率。 这项研究的目标是扩大可以使用中期分析的研究范围，特别强调适合临床试验的方法，这些方法涉及基于随时间推移进行的测量的结果测量。 由于最早分析的数据覆盖的时间跨度有限，此类纵向研究在组序贯监测中提出了独特的问题。 研究过程中随访时间长度的变化可能会对用于确定治疗效果的分析方法的稳健性产生重大影响。该项目将进行的具体研究领域包括：1）制定针对交叉危险的稳健生存数据的测试策略； 2）开发纵向非生存数据的测试策略，该策略对随时间推移的非线性治疗效果具有鲁棒性； 3）开发用于临床试验监测的贝叶斯方法，该方法表现出与生存和其他纵向数据的频率分析所开发的方法类似的鲁棒性特性； 4）概括为多个终点开发的方法，以解决纵向数据问题； 5）推广灵活的监测方案以适应稳健的纵向方法； 6) 开发计算机软件以在临床试验模块中实施这些策略。