Longitudinal Methods for Cardiovascular Disease Research

描述（由申请人提供）：心血管疾病（CVD）的研究是复杂的，涉及快速发展的技术，可以随着时间的推移检测和量化亚临床疾病。改进心血管队列数据的分析将有助于更好地识别心血管疾病的流行病学预测因素，改进风险分层，减少因分析不当而导致的误导性研究。该项目的目的是为了解决目前心血管疾病研究人员面临的方法学问题。我们的目标是开发新的统计工具，可以帮助回答新的科学问题，使用以前由现有的nih资助的研究收集的数据。我们将用两项nhlbi资助的前瞻性队列研究的数据来说明我们的方法：动脉粥样硬化的多民族研究（MESA）和心血管健康研究（CHS），尽管这些方法和模型通常适用于许多研究。我们的拨款将解决心血管研究的两个广泛方面的统计问题，其中创新的分析将允许更好地利用心血管队列研究中包含的信息。首先，我们提出了新的模型来处理研究生物标志物关联时药物使用的方法学挑战。其次，我们开发了改进的冠状动脉钙分析方法，使我们能够更好地利用现有扫描的信息。

DESCRIPTION (provided by applicant): The study of cardiovascular disease (CVD) is complex and involves rapidly evolving technologies that enable detection and quantification of subclinical disease over time. Improved analysis of cardiovascular cohort data will result in better identification of epidemiological predictors of cardiovascular disease, improved risk stratification and fewer misleading studies due to inappropriate analysis. The aims of this project are designed to address current methodological problems facing cardiovascular disease researchers. Our goal is to develop novel statistical tools that can help answer new scientific questions using data that has previously been collected by existing NIH-funded studies. We will illustrate our methods with data from two NHLBI-funded prospective cohort studies: The Multi-Ethnic Study of Atherosclerosis (MESA) and The Cardiovascular Health Study (CHS), although the methods and models are generally applicable to many studies. Our grant will address statistical problems across two broad aspects of cardiovascular research where innovative analysis will allow for better use of the information contained in cardiovascular cohort studies. First, we propose new models to handle the methodologic challenge of medication use when studying biomarker associations. Secondly, we develop improved methods for analysis of coronary artery calcium that allow us make better use of the information from existing scans. PUBLIC HEALTH RELEVANCE: Improved analysis of cardiovascular cohort data will result in better identification of epidemiological predictors of cardiovascular disease, improved risk stratification and fewer misleading studies due to inappropriate analysis. This project will both bring cutting edge statistical techniques into cardiovascular research and foster the development of new statistical techniques designed to solve current methodological problems facing cardiovascular disease researchers.