GROUP SEQUENTIAL METHODS FOR LONGITUDINAL DATA

纵向数据的分组顺序方法

• 批准号: 6646523
• 负责人: Scott S Emerson
• 金额: $ 12.92万
• 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 2005-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6646523
• 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很常见的。 对临床试验数据的这种监测对于确保参与试验的志愿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处理的效率。 本研究的目标是扩大中期分析可以使用的研究范围，特别强调适用于临床试验的方法，这些方法涉及基于随时间推移进行的测量的结局指标。 这种纵向研究在组序贯监测中提出了独特的问题，因为在最早的分析中数据覆盖的时间跨度有限。 研究过程中随访时间的变化可能对用于确定治疗疗效的分析方法的稳健性产生重大影响。该项目的具体研究领域包括：1）开发对交叉风险具有鲁棒性的生存数据检验策略; 2）开发对随时间推移的非线性治疗效应具有鲁棒性的纵向非生存数据检验策略;第三章开发贝叶斯方法用于临床试验监测，其表现出类似于频率主义生存分析的鲁棒性和其他纵向数据; 4）为解决纵向数据问题而开发的多个终点方法的推广; 5）灵活监测方案的推广，以适应稳健的纵向方法;以及6）开发计算机软件，以在临床试验模块中实施这些策略。

项目成果

Group sequential clinical trials for longitudinal data with analyses using summary statistics.

对纵向数据进行分组序贯临床试验，并使用汇总统计进行分析。

• DOI: 10.1002/sim.2127
• 发表时间: 2005
• 期刊: Statistics in medicine
• 影响因子: 2
• 作者: Kittelson,JohnM;Sharples,Katrina;Emerson,ScottS
• 通讯作者: Emerson,ScottS