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

“评估帕金森氏症痴呆症风险的有效统计方法” 摘要/摘要： 建议的R01拨款是为了响应PAR-16-260《行为和社会中的方法论和测量》 科学(R01)“。针对帕金森氏病(PD)痴呆症的疾病修改疗法可能是 在认知能力显著下降之前有效，如已提出的治疗阿尔茨海默病(AD)的建议。因此， 帕金森病的认知生物标记物研究意义重大，因为生物标记物可能预示着未来认知风险的增加。 在标准的神经心理测试中，在可测量的损害之前出现下降。纵向设计特别是 可取的，因为它允许持续监测与认知和 识别对持续或未来认知功能下降最敏感的生物标记物。纵向市场面临的主要挑战 生物标记物研究是很难连续获得每个参与者的所有生物标记物结果，因为 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)忽略丢失的数据， 这导致在存在缺失数据的情况下分析的效率(功率)较低。因此，我们探测到 因此，生物标志物的重大纵向变化受到当前可用的统计方法的限制 效率低下。此R01旨在开发比当前可用方法更有效的纵向方法 存在缺失的生物标记物结果或协变量数据。新方法需要的生物标记物数据将比目前更少 方法达到相同的分析统计能力(效率)。这将是方法上的重大进步，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。它在帕金森病的治疗中有着广泛的应用 以及其他神经退行性疾病，如阿尔茨海默病，以及普通生物医学研究。我们还计划研究 三种潜在的认知生物标志物(脑脊液、脑核磁共振和多巴胺转运体)的研究进展 [DAT]SPECT成像)，并建立它们的时间顺序与帕金森病患者认知功能下降的关系 帕金森进展标记物倡议(PPMI)研究通过应用这些新的统计方法。结果将会是 通知未来研究的设计，测试治疗帕金森病痴呆的可能的疾病修改疗法。

“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.