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Efficient statistical methods for assessing dementia risk in Parkinson's disease

评估帕金森病痴呆风险的有效统计方法

基本信息

批准号：
9925847
负责人：
DANIEL WEINTRAUB
金额：
$ 31.16万
依托单位：
UNIVERSITY OF PENNSYLVANIA
依托单位国家：
美国
项目类别：
财政年份：
2017
资助国家：
美国
起止时间：
2017-07-01 至 2024-04-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9925847
关键词：
Age Alzheimer&apos s Disease Amyloid beta-Protein Anisotropy Anterior Attention Biological Markers Biomedical Research Brain Brain imaging Cerebrospinal Fluid Clinical Cognition Cognitive Computer software Corpus striatum structure Data Deterioration Diffuse Diffusion Magnetic Resonance Imaging Disease Future Grant Impaired cognition Impairment Insula of Reil Internal Capsule Limb structure Magnetic Resonance Imaging Measurable Measurement Measures Medial Memory Methodology Methods Modeling Monitor Neurodegenerative Disorders Neuropsychological Tests Outcome Parietal Lobe Parkinson Disease Parkinson&apos s Dementia Participant Patients Procedures Process Research Resources Risk Signal Transduction Spinal Puncture Statistical Methods Structure Structure of genu of corpus callosum Temporal Lobe Testing Thick Time Work behavioral/social science cerebral atrophy cingulate gyrus cognitive impairment in Parkinson&apos s cohort cost dementia risk design dopamine transporter frontal lobe gray matter improved innovation interest longitudinal design novel novel strategies power analysis progression marker public health research rate of change response single photon emission computed tomography targeted treatment tau Proteins

项目摘要

“Efficient statistical methods for assessing dementia risk in Parkinson's disease” Summary/Abstract: The proposed R01 grant is in response to PAR-16-260 “Methodology and Measurement in the Behavioral and Social Sciences (R01)”. Disease-modifying therapies targeting Parkinson's disease (PD) dementia are likely to be most efficacious before significant cognitive decline has occurred, as has been proposed for Alzheimer's disease (AD). Thus, cognitive biomarker studies in PD are significant because biomarkers may signal an increased risk of future cognitive decline prior to measurable impairment on standard neuropsychological testing. Longitudinal design is particularly desirable because it allows ongoing monitoring of pathophysiological processes associated with cognition and identification of those biomarkers most sensitive to ongoing or future cognitive decline. A major challenge in longitudinal biomarker studies is the difficulty in obtaining all biomarker outcomes serially for every participant, due to limitations in study resources and priorities. Current available statistical procedures such as mixed-effects models ignore missing data, which results in low efficiency (power) of the analyses in the presence of missing data. Thus, our ability to detect significant longitudinal changes in biomarkers is limited by the current available statistical methods due to this inefficiency. This R01 aims to develop more efficient longitudinal methods than the current available methods in the presence of missing biomarker outcome or covariate data. The new methods will require less biomarker data than current methods to achieve the same analytic statistical power (efficiency). This will be a significant methodological advance, as it will reduce future study costs and patient burden without sacrificing power. It has broad applications in PD dementia and other neurodegenerative diseases such as AD, as well as general biomedical research. We also plan to study progression of three potential cognitive biomarkers (cerebrospinal fluid [CSF], brain MRIs, and dopamine transporter [DAT] SPECT imaging) and establish their temporal ordering in relationship to cognitive decline in PD participants in the Parkinson's Progression Markers Initiative (PPMI) study by applying these new statistical methods. The results will inform the design of future studies testing possible disease-modifying therapies in treating PD dementia.

“评估帕金森病痴呆风险的有效统计方法” 摘要/摘要：拟议的 R01 拨款是为了响应 PAR-16-260“行为和社会领域的方法论和测量” 科学 (R01)”。针对帕金森病 (PD) 痴呆症的疾病修饰疗法可能是最重要的在认知能力显着下降之前就有效，正如针对阿尔茨海默病 (AD) 所提出的那样。因此， PD 认知生物标志物研究意义重大，因为生物标志物可能表明未来认知风险增加在标准神经心理学测试出现可测量的损害之前下降。纵向设计特别理想的，因为它允许持续监测与认知和相关的病理生理过程识别对当前或未来认知能力下降最敏感的生物标志物。纵向的主要挑战生物标志物研究的难点在于，由于研究的局限性，连续获得每个参与者的所有生物标志物结果是很困难的。研究资源和优先事项。当前可用的统计程序（例如混合效应模型）忽略了缺失的数据，这会导致在存在缺失数据的情况下分析效率（能力）低下。因此，我们的检测能力由于这一原因，生物标志物的显着纵向变化受到当前可用统计方法的限制效率低下。该 R01 旨在开发比当前可用方法更有效的纵向方法存在缺失的生物标志物结果或协变量数据。新方法将比当前方法需要更少的生物标志物数据实现相同分析统计功效（效率）的方法。这将是方法论上的重大进步，因为它将在不牺牲力量的情况下减少未来的研究成本和患者负担。它在帕金森病痴呆症中有广泛的应用和其他神经退行性疾病，如 AD，以及一般生物医学研究。我们也打算学习三种潜在认知生物标志物（脑脊液 [CSF]、脑 MRI 和多巴胺转运蛋白）的进展 [DAT] SPECT 成像）并建立与 PD 参与者认知能力下降相关的时间顺序帕金森病进展标记计划 (PPMI) 通过应用这些新的统计方法进行研究。结果将为未来测试治疗帕金森病痴呆的可能疾病缓解疗法的研究设计提供信息。