Optimal design and analysis of AD treatment

描述（由主要研究者提供）：美国食品和药物管理局最近放宽了对研究性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。具体而言，使用所有纵向数据点（而不仅仅是第一个和最后一个）估计斜率的分析，现已报告用于治疗阿尔茨海默病的研究性新药。一个代表性的例子是最近报道的bapineuzumab治疗阿尔茨海默病试验，该试验测试了治疗对全球认知评估量表下降率的影响。该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中。本分析计划的样本量考虑不充分，本分析中隐含的许多假设从未用代表性阿尔茨海默病数据进行过正式检验。有两个独特的数据资源可用于探讨这些问题。一个数据资源是来自阿尔茨海默病合作研究（ADCS）的累积临床试验数据。ADCS对不属于FDA管辖范围的非许可治疗进行临床试验，在变化率分析方面拥有十多年的经验。第二个数据来源是阿尔茨海默病神经影像学倡议（ADNI）队列，该队列的创建明确旨在为未来轻度认知障碍（MCI）受试者的阿尔茨海默病治疗试验和二级预防试验的设计提供信息。为此，我们提出以下具体目标：具体目标1。使用阿尔茨海默病合作研究（ADCS）和阿尔茨海默病神经影像学倡议（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病治疗试验和二级预防（MCI）试验的统计样本量要求。具体目标2。使用来自ADCS和ADNI的数据，描述各种生物标志物的潜在相对效用，这些生物标志物被提议作为阿尔茨海默病治疗试验和二级预防（MCI）试验的替代结局指标。具体目标3。利用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，特别是随机脱落假设和随时间线性进展假设。具体目标4。使用来自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分析相对于对随机脱落假设失效具有稳健性的替代方法的性能。公共卫生相关性：太小的临床试验是没有信息的，容易出现“假阴性”，即错误的结论，有效的治疗是无效的。太大的试验会导致不必要的研究受试者负担和成本，这是一个不可忽视的问题，因为阿尔茨海默病治疗试验需要数百名受试者和数百万美元来执行。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。

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.