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

• 批准号: 9366254
• 负责人: DANIEL WEINTRAUB
• 金额: $ 33.48万
• 依托单位: UNIVERSITY OF PENNSYLVANIA
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-07-01 至 2021-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9366254
• 关键词: Age; Alzheimer' 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; Dementia; 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' s Dementia; Participant; Patients; Procedures; Process; Research; Resources; Risk; Signal Transduction; Spinal Puncture; Statistical Methods; Structure of genu of corpus callosum; Temporal Lobe; Testing; Thick; Time; Work; behavioral/social science; cerebral atrophy; cingulate gyrus; cohort; cost; 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

“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” 总结/摘要： 建议的R 01赠款是为了响应PAR-16-260“行为和社会 科学（R 01）"。针对帕金森病（PD）痴呆的疾病修饰疗法可能是最 在发生显著的认知下降之前有效，如已经针对阿尔茨海默病（AD）提出的。因此，在本发明中， PD中的认知生物标志物研究是重要的，因为生物标志物可能预示着未来认知障碍的风险增加。 在标准神经心理学测试中出现可测量的损伤之前下降。纵向设计特别 因为它允许持续监测与认知相关的病理生理过程， 鉴定对正在进行的或未来的认知衰退最敏感的生物标志物。纵向的一个重大挑战 生物标志物研究的一个困难是，由于生物标志物研究的局限性， 研究资源和优先事项。目前可用的统计程序，如混合效应模型，忽略了缺失数据， 这导致在存在缺失数据的情况下分析的低效率（功效）。因此，我们探测 生物标志物的显著纵向变化受到当前可用的统计方法的限制， 效率低下。本R 01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局或协变量数据。新方法需要的生物标记物数据将比目前更少 方法来实现相同的分析统计功效（效率）。这将是一个重大的方法进步，因为 它将在不牺牲功率的情况下降低未来的研究成本和患者负担。它在PD痴呆中具有广泛的应用 和其他神经退行性疾病，如AD，以及一般生物医学研究。我们还计划研究 三种潜在的认知生物标志物（脑脊液[CSF]、脑MRI和多巴胺转运蛋白）的进展 [DAT]SPECT成像），并建立它们与PD参与者认知下降相关的时间顺序。 帕金森病进展标志物倡议（PPMI）研究通过应用这些新的统计方法。结果将 为未来研究的设计提供信息，以测试治疗PD痴呆的可能疾病修饰疗法。