GROUP SEQUENTIAL METHODS FOR LONGITUDINAL DATA

纵向数据的分组顺序方法

• 批准号: 6231283
• 负责人: Scott S Emerson
• 金额: $ 13.98万
• 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/6231283
• 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是司空见惯的。这种对临床试验数据的监测对于确保参与试验的志愿者患者得到合乎伦理的治疗至关重要。此外，使用分组顺序停止规则可以大大提高采用新处理的效率。本研究的目标是拓宽可用于中期分析的研究范围，特别强调适用于临床试验的方法，包括基于时间测量的结果测量。这种纵向研究在群体序贯监测中提出了独特的问题，因为在最早的分析中，数据所涵盖的时间范围有限。在整个研究过程中，随访时间的变化会对用于确定治疗疗效的分析方法的稳健性产生重大影响。在这个项目下进行的具体研究领域包括：1)开发对交叉危险具有稳健性的生存数据的测试策略；2)开发纵向非存活数据的测试策略，使其对非线性治疗效应具有鲁棒性；3)发展用于临床试验监测的贝叶斯方法，显示出与用于生存率和其他纵向数据的频率分析类似的鲁棒性；4)推广为多个端点开发的方法，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，在临床试验模块中实施这些策略。