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

生物标志物和诊断测试评估的统计发展及其在阿尔茨海默病中的应用

• 批准号: 10530697
• 负责人: Katelyn A McKenzie
• 金额: $ 5.27万
• 依托单位: UNIVERSITY OF KANSAS MEDICAL CENTER
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-12-30 至 2024-12-29
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10530697
• 关键词: Adopted; Affect; Agreement; Algorithms; Alzheimer disease prevention; Alzheimer' s Disease; Alzheimer' s disease diagnosis; Alzheimer' s disease model; Alzheimer’s disease biomarker; Award; Biological Markers; Brain imaging; Characteristics; Clinical; Clinical Research; Communities; Complex; Data; Data Collection; Data Set; Dementia; Dependence; Development; Diagnosis; Diagnostic; Diagnostic tests; Disease; Disease Progression; Doctor of Philosophy; Enrollment; Environment; Evaluation; Exercise; Experimental Designs; Foundations; Funding; Future; Goals; Growth; Histologic; Image; Literature; Measures; Medical; Medicine; Methodology; Methods; Modeling; Physicians; Prevalence; Prevention program; Probability; Provider; Recommendation; Research; Research Design; Research Training; Risk Adjustment; Risk Factors; Sampling; Science; Sensitivity and Specificity; Source; Statistical Methods; Testing; Training; United States National Institutes of Health; base; biomarker development; biomarker performance; career; clinical training; design; diagnostic biomarker; diagnostic strategy; evaluation/testing; flexibility; imaging biomarker; improved; in silico; neuroimaging; neuroimaging marker; novel; novel diagnostics; novel marker; pre-doctoral; predictive modeling; programs; recruit; simulation; success; tool

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.

项目摘要 协议研究，用于评估新开发的生物标志物和诊断测试的研究，具体取决于 根据大约50年前提出的统计方法。由于阿尔茨海默氏病已经进入了擦除 神经影像学生物标志物已纳入诊断策略，我们的统计数据的进展 方法必须与我们的医疗增长相匹配。这些统计方法包含重大缺陷，例如 取决于样本疾病患病率，具有复杂的解释，施加了限制性实验 设计和无法解释危险因素。当前的建议旨在推进统计方法 用于开发生物标志物和诊断，并这样做将确定重要的特征 用于诊断和预测阿尔茨海默氏病的状况。 我们假设我们的新颖统计贡献将在我们的生物标志物方法中纠正这些缺陷 和诊断测试开发。它将允许一种更有信息，可解释和健壮的方法来量化 医疗测试的协议和准确性。为了我们的第一个目标，我们将开发一种新颖的统计方法 为了量化一致性，将我们的框架集中在混合效应模型上。我们将将我们的方法与 评估神经影像生物标志物的提供者之间的传统方法。我们将利用NIA-的数据 通过锻炼（APEX）研究资助了R01阿尔茨海默氏症的预防，并通过主要数据收集来增强它 提供者的潜水员样本的神经影像学解释。为了我们的第二个目标，我们将确定如何 当代协议研究的抽样设计影响预测准确性。仿真“内部” 将进行研究，再现文献中存在的许多情况。此外，来自 阿尔茨海默氏病神经影像学倡议（ADNI）将被掌握，并衡量准确性，这样的措施 作为灵敏度和特异性，将被计算。我们将在传统方法和我们的小说中这样做 统计框架表明当前方法只是我们更强大模型的特殊情况。 这将突出当前方法的局限性。 该提议的广泛，长期目标是两个方面。首先，我们旨在发展一个强大的 评估生物标志物和诊断测试的一致性和准确性的广义统计方法。经过 使用混合效果模型和相应的抽样设计，我们将克服传统中存在的缺陷 方法和获得优势，例如易于解释的措施和可推广的结果。第二个 目的是促进严格的培训计划，该计划将为我未来的职业提供基础 专注于生物标志物方法论的医师 - 统计家。而这些目标的结合 出于我当前的MD-PHD计划和该NIH博士前培训F30奖的目的，统计 进步适用于任何医疗状况，因此被中毒以对 生物医学科学。