权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

Measuring change in Alzheimer's disease dysfunction more precisely with IRT

通过 IRT 更精确地测量阿尔茨海默病功能障碍的变化

基本信息

批准号：
8302649
负责人：
Steve Balsis
金额：
$ 7.31万
依托单位：
TEXAS A&M UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2012
资助国家：
美国
起止时间：
2012-04-01 至 2014-03-31
项目状态：
已结题

项目摘要

DESCRIPTION (provided by applicant): There are several measures that are regularly used to assess cognitive and behavioral dysfunction in basic Alzheimer's disease (AD) research and in clinical trials of Alzheimer's disease medications. All of these measures rely on total raw scores to assess dysfunction. That is, a person receives a score that reflects the sum of the individual items. There are major problems, however, with this standard approach that limit how precisely one can assess true levels of dysfunction and changes in dysfunction that accompany AD. The major problem is that the current raw score approach weights each item on a measure equally; that is, each item is assumed to contribute equally to the total score. But, in actuality, test items often differ in their difficulty and/or strength of relationship to dysfunction, which means that individual test items differ in their ability to measure dysfunction. Item-Response Theory (IRT)-based analyses are specifically designed to consider these item differences, and should be able to help us more precisely assess dysfunction. Aim #1 of the proposed research is to use Item Response Theory (IRT)-based scoring to more precisely assess change in AD-related dysfunction. To do this we will extend our preliminary cross-sectional findings for this measure, the MMSE, and the CDR, to examine data longitudinally in Baylor College of Medicine's Alzheimer's Disease and Memory Disorder Center comprehensive dementia database. We anticipate that these data will confirm longitudinally that IRT analyses can provide more precise information about change in AD dysfunction than raw scores. If this is true, then IRT analyses has the potential not only to detect change in AD dysfunction more precisely than the standard raw score approach, but it could result in significant cost savings for AD basic research and clinical trials of AD medications. Aim #2 is to quantify the number of participants needed to detect change that is gained by using an IRT-based scoring approach compared to a raw score approach. To fulfill this aim we will create a table for each measure (ADAS-cog, MMSE, CDR) and each possible different raw score change on these measures (1-point change, 2-point change, 3-point change, 4-point change, etc.). Then, we will determine the effect sizes associated with these changes using a raw score versus an IRT scoring approach. The IRT scoring approach, because of its greater sensitivity, should yield larger effect sizes (and hence require fewer participants) than the raw score approach. The findings from Aim #2 would be useful for many researchers, including those running clinical drug trials, who for instance might want to determine the precise number of participants needed to detect a certain sized change (3-point change) on one of these measures. These researchers could use these tables to easily compare the number of participants needed to detect different changes if IRT vs. raw scoring were used. PUBLIC HEALTH RELEVANCE: While it is standard practice to assess Alzheimer's disease (AD) dysfunction using raw scores, there are major problems with this practice that limit how precisely one can measure dysfunction or changes in dysfunction that accompany AD. The overarching goal of the proposed research is to use a new scoring approach to improve our ability to precisely and efficiently measure dysfunction associated with AD and improves our ability to detect both individual and group change in AD basic research and clinical trials of AD medications. Because this new scoring system can more precisely detect change than a raw score system, fewer participants would be needed to run a clinical trial, for example, meaning that we would not only be able to detect change in dysfunction associated with the disease more precisely, but also potentially save money when conducting research.

描述(申请人提供)：在阿尔茨海默病(AD)的基础研究和阿尔茨海默病药物的临床试验中，有几种指标经常用于评估认知和行为功能障碍。所有这些措施都依赖于总的原始分数来评估功能障碍。也就是说，一个人收到的分数反映了各个项目的总和。然而，这种标准方法存在重大问题，限制了人们如何准确地评估真实的功能障碍水平和伴随AD的功能障碍的变化。主要的问题是，当前的原始计分方法在衡量标准上对每个项目进行了平等的加权；也就是说，假设每个项目对总得分的贡献是相等的。但是，实际上，测试项目通常在与功能障碍的关系的难度和/或强度上有所不同，这意味着各个测试项目测量功能障碍的能力不同。基于项目反应理论(IRT)的分析是专门为考虑这些项目差异而设计的，应该能够帮助我们更准确地评估功能障碍。研究的目的1是使用基于项目反应理论(IRT)的评分来更准确地评估AD相关功能障碍的变化。为此，我们将扩展我们对这项测量的初步横断面研究结果，MMSE和CDR，以纵向检查贝勒医学院阿尔茨海默病和记忆障碍中心综合痴呆症数据库中的数据。我们预计，这些数据将纵向证实，与原始评分相比，IRT分析可以提供关于AD功能障碍变化的更准确的信息。如果这是真的，那么IRT分析不仅有可能比标准的原始评分方法更准确地检测AD功能障碍的变化，而且它还可以为AD基础研究和AD药物的临床试验节省大量成本。目标2是量化通过基于IRT的计分方法与原始计分方法相比，检测变化所需的参与者数量。为了实现这一目标，我们将为每个测量(ADAS-COG、MMSE、CDR)和这些测量上每个可能的不同原始分数变化(1分变化、2点变化、3点变化、4点变化等)创建一个表。然后，我们将使用原始评分与IRT评分方法确定与这些变化相关的影响大小。IRT计分方法由于其更高的敏感性，应该比原始计分方法产生更大的效应大小(因此需要更少的参与者)。AIM#2的发现将对许多研究人员有用，包括那些进行临床药物试验的研究人员，例如，他们可能想要确定在这些测量之一上检测特定大小变化(3点变化)所需的参与者的确切数量。如果使用IRT和原始评分，这些研究人员可以使用这些表格轻松比较检测不同变化所需的参与者数量。公共卫生相关性：虽然使用原始分数评估阿尔茨海默病(AD)功能障碍是标准做法，但这种做法存在重大问题，限制了人们如何准确地衡量AD伴随的功能障碍或功能障碍的变化。拟议研究的总体目标是使用一种新的评分方法来提高我们精确和有效地测量与AD相关的功能障碍的能力，并提高我们在AD基础研究和AD药物临床试验中检测个体和群体变化的能力。例如，由于这种新的评分系统可以比原始评分系统更准确地检测变化，因此进行临床试验所需的参与者将更少，这意味着我们不仅能够更准确地检测与疾病相关的功能障碍的变化，而且在进行研究时还有可能节省资金。