Statistical developments for biomarker and diagnostic test evaluation with applications to Alzheimer's disease

项目摘要 用于评估新开发的生物标志物和诊断测试的一致性研究取决于 近50年前提出的统计方法。随着阿尔茨海默病进入一个时代， 神经影像学生物标志物被纳入诊断策略，我们的统计学进展 方法必须与我们的医学发展相匹配。这些统计方法包含重大缺陷，例如 取决于样本疾病流行率，具有复杂的解释，实施限制性实验 设计和未能考虑到风险因素。目前的建议旨在改进统计方法， 用于生物标志物和诊断的发展，并在这样做，将确定重要的特征， 用于诊断和预测阿尔茨海默病的状态。 我们假设，我们新的统计贡献将纠正我们的生物标志物方法中的这些缺陷， 和诊断测试开发。它将提供一种信息量更大、更可解释和更有力的方式来量化 医学测试的一致性和准确性。对于我们的第一个目标，我们将开发一种新的统计方法 量化协议，集中我们的框架上的混合效应模型。我们将比较我们的方法与 评估神经影像学生物标志物的提供者之间的传统方法。我们将利用国家情报局的数据- 资助的R 01通过运动预防阿尔茨海默氏症（APEx）研究，并通过原始数据收集加强它 神经影像学解释的不同样本的供应商。对于我们的第二个目标，我们将确定如何 当代一致性研究的抽样设计影响预测准确性。模拟“计算机模拟” 将进行研究，再现文献中提出的许多设想。此外，来自 阿尔茨海默病神经影像学倡议（ADNI）将被利用和措施，告知准确性， 将计算灵敏度和特异性。我们将在传统方法和我们的小说中这样做。 统计框架，表明目前的方法只是我们更强大的模型的一个特例。 这将突出当前方法的局限性。 这项建议的广泛和长期目标是双重的。首先，我们的目标是建立一个强大的， 用于评价生物标志物和诊断测试的一致性和准确性的通用统计方法。通过 使用混合效应模型和相应的抽样设计，我们将克服传统方法中存在的缺陷， 方法，并获得优势，如易于解释的措施和推广的结果。第二 我的目标是促进一个严格的培训计划，这将为我未来的职业生涯奠定基础， 专注于生物标志物方法学的医生-统计学家。虽然这些目标的结合 与我目前的MD-PhD计划和这个NIH博士前培训F30奖的目的，统计 这些进步适用于任何医疗条件，因此有望对 生物医学科学

Project Summary Agreement studies, used in the evaluation of newly-developed biomarkers and diagnostic tests, depend upon statistical methods proposed nearly 50 years ago. As Alzheimer’s disease has entered into an era where neuroimaging biomarkers are incorporated into the diagnostic strategy, the progression of our statistical methods must match that of our medical growth. These statistical methods contain significant flaws, such as depending on sample disease prevalence, having complex interpretations, imposing restrictive experimental designs and failing to account for risk factors. The current proposal seeks to advance the statistical methods used in the development of biomarkers and diagnostics, and, in doing so, will identify characteristics important for diagnosing and predicting Alzheimer’s disease status. We hypothesize that our novel statistical contributions will correct these flaws in our approach to biomarker and diagnostic test development. It will allow for a more informative, interpretable and robust way to quantify the agreement and accuracy of medical tests. For our first aim, we will develop a novel statistical methodology to quantify agreement, centering our framework on mixed effect models. We will compare our approach with traditional methods among providers evaluating neuroimaging biomarkers. We will leverage data from the NIA- funded R01 Alzheimer’s Prevention through Exercise (APEx) study and enhance it with primary data collection of neuroimaging interpretations by a diverse sample of providers. For our second aim, we will determine how the sampling design of contemporary agreement studies influence predictive accuracy. Simulation “in-silico” studies will be performed, reproducing many scenarios present in the literature. Additionally, data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) will be leveraged and measures informing accuracy, such as sensitivity and specificity, will be calculated. We will do so under traditional approaches and our novel statistical framework, demonstrating that current methods are merely a special case of our more robust model. This will highlight limitations of current approaches. The broad, long term objectives of this proposal are two-fold. First, we aim to develop a robust and generalized statistical method for evaluating agreement and accuracy of biomarkers and diagnostic tests. By using mixed effects models and corresponding sampling designs, we will overcome flaws present in traditional approaches and gain advantages, such as easily interpreted measures and generalizable results. The second objective is to facilitate a rigorous training plan that will provide a foundation for my future career as a physician-statistician who focuses on biomarker methodology. While the combination of these objectives aligns with the purpose of my current MD-PhD program and this NIH Pre-doctoral Training F30 award, the statistical advancements are applicable to any medical condition, and thus are poised to have a strong impact on biomedical science.