Core C: Data Management, Biostatistics and Modelling

CORE C.数据管理、生物统计学和建模 总结/摘要 ASPREE保持了出色的数据质量（99.6%的准确率），并进行了关键的生物统计分析， 作为ASPREE临床试验的一部分，收集了大量数据（约2600万个数据值）。这个核心将建立 通过扩大能力、可及性和方法的范围，对ASPREE的成功进行评估。将实施 并维持一个先进的技术平台，以支持：i）实地数据收集（核心A的重点）; ii） 促进临床事件和文件的捕获（核心A和B）; iii）支持临床事件的裁定 (Core B）; iv）提供临床图像和生物样本数据的存储和分析（核心D）; v）支持链接 与国家和州数据存储库（管理核心）; vi）将参与者措施和临床 数据（核心A和B）与分析就绪的生物标本（核心D）和基因组（核心E）数据;和vii）支持研究 管理和治理（参见研究战略中的图1）。Core C还将开发一个 生物统计和建模专业知识，以支持对疾病和衰老纵向数据的复杂分析 U19项目1-3的流程。具体来说，这个核心将应用状态转换模型来解开 参与者从无癌症到各种癌症状态的转变，包括癌症死亡， 重点关注长期使用LDA的影响;完整的连续生物标志物和健康指标跟踪， 分析认知变化的轨迹;开发阿尔茨海默病风险和认知的预测模型 针对老年人的弹性，结合生活方式，健康，基因组和生物标志物数据进行分析 认知能力下降;进行持续的生物标志物跟踪分析，以分析基于评分的指标的变化 研究虚弱的发展和进展以及虚弱与 残疾;并采用多状态时间-事件模型来分析残疾的复杂疾病途径。的 研究结果的影响将通过投影建模来探索，年轻的研究人员将接受最好的培训， 实践数据管理、数据科学、生物统计学和建模。核心C的活动对于 探索与癌症、阿尔茨海默病和残疾有关的结果。因此，核心活动 C是解决U19研究目标的基础。

CORE C. DATA MANAGEMENT, BIOSTATISTICS AND MODELLING SUMMARY/ABSTRACT ASPREE maintained excellent data quality (99.6% accuracy) and performed crucial biostatistical analysis for a vast quantity of data (~26 million data values) collected as part of the ASPREE Clinical trial. This Core will build on the success of ASPREE by expanding capability, accessibility and methodological reach. It will implement and maintain a sophisticated technology platform to support: i) in-field data collection (the focus of Core A); ii) facilitate the capture of clinical events and documents (Cores A & B); iii) support adjudication of clinical events (Core B); iv) provide storage and analysis of clinical images and biospecimen data (Core D); v) support linkage with national and state data repositories (Admin Core); vi) undertake linkage of participant measures and clinical data (Core A & B) with analysis-ready biospecimen (Core D) and genomic (Core E) data; and vii) support study administration and governance (refer to Figure 1 in Research Strategy). Core C will also develop a hub of biostatistical and modeling expertise to support the complex analysis of longitudinal data on disease and aging processes across Projects 1-3 of the U19. Specifically, this Core will apply state transition models to disentangle transitions of participants from being cancer-free to various cancer states, including death from cancer, with a key focus on the effect of long-term LDA use; complete continuous biomarker and health measure tracking to analyze trajectories of cognitive change; develop prediction models for Alzheimer’s disease risk and cognitive resilience, specific to older people, incorporating lifestyle, health, genomic and biomarker data to analyze cognitive decline; conduct continuous biomarker tracking analysis to analyze changes in score-based measures of health to investigate the development and progression of frailty and the relationship between frailty and disability; and employ multi-state time-to-event models to analyze complex disease pathways for disability. The impact of findings will be explored through projection modeling, and young investigators will be trained in best practice data management, data science, biostatistics and modeling. The activity of Core C is essential for the exploration of outcomes related to cancer, Alzheimer ’s disease and disability. Consequently, the activity of Core C is fundamental to addressing the research Aims for this U19.