Optimal design and analysis of AD treatment

AD治疗的优化设计与分析

• 批准号: 7708138
• 负责人: Steven Dyal Edland
• 金额: $ 5.54万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN DIEGO
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-08-15 至 2011-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7708138
• 关键词: Alzheimer' s Disease; Biological Markers; Clinical; Clinical Trials; Cognitive; Cohort Studies; Data; Dropout; Equation; Failure; Future; Impaired cognition; Investigational Drugs; Methods; Metric; Mind; Modeling; Outcome; Outcome Measure; Performance; Principal Investigator; Relative (related person); Reporting; Resources; Sample Size; Sampling; Secondary Prevention; Study Subject; Testing; Time; United States Food and Drug Administration; United States National Institutes of Health; cohort; cooperative study; cost; design; effective therapy; experience; follow-up; neuroimaging; public health relevance; treatment effect; treatment trial

DESCRIPTION (provided by principal investigator): The Food and Drug Administration has recently relaxed its rules governing statistical analysis plans for clinical trials of investigational new drugs to allow rate of change analysis. Specifically, analyses that estimate slope using all longitudinal data points, not just first and last, have now been reported for investigational new drugs to treat Alzheimers disease. A representative example is the recently reported Alzheimers disease treatment trial of bapineuzumab, which tested the effect of treatment on rate of decline on a global cognitive assessment scale. The analysis used a modied intent to treat (MITT) sample, restricting the analysis to subjects with at least one follow-up observation. Sample size considerations are poorly developed for this analysis plan, and the many assumptions implicit in this analysis have never been formally tested with representative Alzheimers disease data. Two unique data resources are available to explore these issues. One data resource is the accumulated clinical trial data from the Alzheimers Disease Cooperative Study (ADCS). The ADCS, which performs clinical trials of non-licensible treatments not under the purview of the FDA, has over ten years of experience with the rate of change analysis. The second data resource is the Alzheimers Disease Neuroimaging Initiative (ADNI) cohort, which was created expressly for the purpose of informing the design of future Alzheimers disease treatment trials and secondary prevention trials of mild cognitively impaired (MCI) subjects. With this in mind, we propose the following Specific Aims: Specific Aim 1. Using data from the Alzheimers Disease Cooperative Study (ADCS) and the Alzheimers Disease Neuroimaging Initiative (ADNI), to accurately determine statistical sample size requirements for Alzheimer treatment trials and secondary prevention (MCI) trials using standard clinical and neuropsychometric outcomes. Specific Aim 2. Using data from ADCS and ADNI, to describe the potential relative utility of various biomarkers proposed as surrogate outcome measures for Alzheimer treatment trials and secondary prevention (MCI) trials. Specific Aim 3. Using data from ADCS and ADNI, to test the validity of the statistical assumptions implicit in the MITT rate of change analysis, specically, the assumption of random dropout and the assumption of linear progression over time. Specific Aim 4. Using data from ADCS and ADNI, to explore the performance of standard MITT analyses using mixed effects models or generalized estimating equations relative to alternative methods that are robust to failures of the random dropout assumptions. PUBLIC HEALTH RELEVANCE: Clinical trials that are too small are noninformative and prone to 'false negatives', that is, erroneous conclusions that an effective treatment is ineffective. Trials that are too large incur unnecessary study subject burden and cost, a not inconsequential concern, as Alzheimer treatment trials require hundreds of subjects and millions of dollars to perform. We will use accumulating data from NIH sponsored cohort studies and treatment trials to determine optimal samples size for future clinical trials.

描述(由首席研究员提供)：食品和药物管理局最近放宽了对研究中新药临床试验的统计分析计划的管理规则，允许进行变化率分析。具体地说，使用所有纵向数据点来估计斜率的分析，而不仅仅是第一次和最后一次，现在已经被报道用于治疗阿尔茨海默病的研究新药。一个有代表性的例子是最近报道的bapineuzumab的阿尔茨海默病治疗试验，该试验在全球认知评估表上测试了治疗对下降率的影响。这项分析使用了改良的意向治疗(MITT)样本，将分析限制在至少有一次随访观察的受试者身上。这项分析计划的样本量考虑很差，而且这项分析中隐含的许多假设从未用有代表性的阿尔茨海默病数据进行过正式测试。有两个独特的数据资源可用于探索这些问题。一个数据来源是阿尔茨海默病合作研究(ADCS)积累的临床试验数据。ADCS对不在FDA职权范围内的非许可治疗进行临床试验，在变化率分析方面有十多年的经验。第二个数据来源是阿尔茨海默氏病神经成像倡议(ADNI)队列，它是专门为设计未来阿尔茨海默氏病治疗试验和轻度认知障碍(MCI)受试者的二级预防试验而创建的。考虑到这一点，我们提出了以下具体目标：具体目标1.使用阿尔茨海默氏病合作研究(ADCS)和阿尔茨海默氏病神经成像倡议(ADNI)的数据，使用标准临床和神经心理测量结果准确确定阿尔茨海默氏症治疗试验和二级预防(MCI)试验的统计样本量要求。具体目的2.使用ADCS和ADNI的数据，描述作为阿尔茨海默病治疗试验和二级预防(MCI)试验替代结果指标的各种生物标记物的潜在相对实用性。具体目标3.使用ADCS和ADNI的数据，检验Mitt变化率分析中隐含的统计假设的有效性，特别是随机辍学假设和随时间线性递增的假设。具体目的4.使用ADCS和ADNI的数据，探索使用混合效应模型或广义估计方程的标准MITT分析相对于对随机辍学假设失败的替代方法的性能。与公共卫生相关：规模太小的临床试验没有信息量，容易出现“假阴性”，即错误地得出有效治疗无效的结论。规模太大的试验会招致不必要的研究对象负担和成本，这不是一个无关紧要的问题，因为阿尔茨海默氏症治疗试验需要数百名受试者和数百万美元才能进行。我们将使用NIH赞助的队列研究和治疗试验的累积数据来确定未来临床试验的最佳样本量。