Methodological Development and Secondary Data Analysis for Early Stage Alzheimer’s Disease Studies

项目总结/摘要 在研究的早期阶段，通常会收集试点数据，以确定研究计划是否进行后续研究。 更大规模的试验是可行的;然而，在收集研究假设之后， 初始数据是一个难题。传统的假设检验不太适合这项任务，由于断开 假设检验的目的是为感兴趣的终点提供确证性证据， 需要进行初步研究分析，以确定后续试验是否合理。其他方法已经 但这些通常需要了解最小临床重要差异（MCID）， MCID的研究通常不为人所知。研究人员需要一种方法来确定他们的理论是否被接受 仅基于观察到的导频数据。此外，这些早期的分析往往会因 多个感兴趣的终点的集合。我们开发了一个全球性的假设检验，预测检验， 预期用于与样本量相关的多个目标终点， 相关性，控制了整个系列的错误率，并且随着端点数量的增加，功效也会增加。 我们建议扩展预测测试的方法，使其更强大，并允许它 可以用于更多的设置。具体来说，我们将扩展该方法以允许更多类型的预测， 允许对终点进行优先排序，说明观察到的效应的大小，并增加总体把握度 通过解决当终点数量非常小时测试的保守性。使用这些 方法的发展，我们将进行二次数据分析两个阿尔茨海默病试点 问题研究这将使我们能够重新审视原来的假设，处理有氧运动的影响， 运动对阿尔茨海默病的结果，以及有氧运动在临床前的剂量反应潜力 老年痴呆症患者。预测检验可以更好地解决原始研究假设， 合并所有终点（主要终点、次要终点和探索性终点）的信息。另夕h 预测检验非常适合于评估许多终点之间的小但一致的影响， 提出了一个比传统的集中趋势变化更合适的假设，即我们提出了 研究者的理论是否能预测数据的假设。因此，重新分析这些 使用更强大和适当的测试的研究将提供更好的了解有氧运动的影响， 运动对AD和临床前AD患者的整体健康（认知、身体和功能）的影响。

Project Summary/Abstract In the early stages of research pilot data is often collected to determine if the research plan for a follow-up larger trial is feasible; however, determining if the research hypothesis itself is plausible after collecting the initial data is a difficult problem. Traditional hypothesis testing is poorly suited to this task due to the disconnect between the goal of hypothesis tests, to provide confirmatory evidence on the endpoints of interest, and the need for the pilot study analysis, to determine if a follow-up trial is justified. Other approaches have been proposed but these often require knowledge of a minimum clinically important difference (MCID), in novel research an MCID is not typically known. Researcher's need a way to determine if their theory is being borne out based only on the observed pilot data. Additionally, these early analyses are often complicated by the collection of multiple endpoints of interest. We've developed a global hypothesis test, the prediction test, which is intended for use with many endpoints of interest relative to the sample size, allows for the endpoints to be correlated, controls the family-wise error rate, and increases in power as the number of endpoints increases. We propose extensions to the methodology of the prediction test that will make it more powerful and allow it to be used in more settings. Specifically, we will extend the methodology to allow for more types of predictions, allow the prioritization of endpoints, account for the magnitude of observed effects and increase overall power by addressing the conservative nature of the test when the number of endpoints is very small. Using these methodological developments, we will perform a secondary data analysis on two Alzheimer's Disease pilot studies. This will enable us to re-examine the original hypotheses which dealt with the effect of aerobic exercise on Alzheimer's outcomes, and the potential of a dose-response for Aerobic exercise in pre-clinical Alzheimer's Disease patients. The prediction test can better address the original research hypotheses by combining the information across all endpoints, both primary, secondary, and exploratory. Additionally, the prediction test is well suited to the evaluation of small but consistent effects across many endpoints and addresses a more appropriate hypothesis than the traditional change in central tendency, namely, we address the hypothesis of whether the researcher's theory is predictive of the data. Thus, the re-analysis of these studies using a more powerful and appropriate test will provide better understanding of the effects of aerobic exercise on the overall health (cognitive, physical and function) of both AD and pre-clinical AD patients.