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

项目摘要/摘要 在研究的早期阶段，通常会收集先导数据，以确定是否有后续研究计划 更大规模的试验是可行的；然而，在收集了 初始数据是一个难题。由于这种脱节，传统的假设检验不太适合这项任务 假设检验的目标是为感兴趣的终结点提供确凿证据，而 需要进行试点研究分析，以确定后续试验是否合理。其他方法包括 但在小说中，这些通常需要最小临床重要差异(MCID)的知识 研究MCID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得到了证实 仅基于观察到的飞行员数据。此外，这些早期的分析通常会因为 关注的多个终结点的集合。我们已经开发了一个全球假设检验，即预测检验，它 旨在与相对于样本大小的许多感兴趣的端点一起使用，从而允许端点 相关，控制家族级错误率，并随着终端数量的增加而增加功率。 我们建议对预测测试的方法进行扩展，使其更强大，并允许它 将在更多的环境中使用。具体地说，我们将扩展该方法，以允许更多类型的预测， 允许确定终端的优先顺序，考虑到观察到的影响的大小，并提高总体能力 通过解决端点数非常少时测试的保守性。使用这些 方法的发展，我们将对两名阿尔茨海默病飞行员进行二次数据分析 学习。这将使我们能够重新检验涉及有氧运动影响的原始假设。 运动对阿尔茨海默病预后的影响以及临床前有氧运动的剂量反应潜力 阿尔茨海默病患者。预测检验可以通过以下方式更好地解决原始研究假设 合并所有终端的信息，包括主要终端、次要终端和探索性终端。此外， 预测测试非常适合评估跨越多个端点的小但一致的影响 提出了一个比传统的中心趋势变化更合适的假设，即我们解决了 研究人员的理论是否对数据具有预测性的假设。因此，对这些问题的重新分析 使用更强大和更合适的测试的研究将提供对有氧运动影响的更好的理解 运动对AD患者和临床前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.