The case time series design: a new tool for big data analysis

生物医学研究已经被最近大数据技术的发展所改变。例如，在链接的电子数据库中收集的健康记录提供了关于大样本患者的人口统计学、健康事件、药物和生活方式因素的信息。类似地，诸如移动的电话之类的便携式设备提供了招募大量参与者以及收集实时和地理定位的个人水平测量的可能性。虽然这些资源提供了可能性，以回答研究问题，不能切实解决使用传统的研究，他们需要创新的分析方法。本项目有助于解决这个问题，通过开发一种新的分析方法称为案例时间序列设计。这种设计结合了现有方法的特点，以生成一种适应性更强的工具，特别适合分析信息量大的大数据资源。病例时间序列设计适用于不同的研究领域，用于调查与各种风险因素（如环境暴露、临床状况或药物使用）相关的健康影响。该研究提案的结构在一个详细的工作计划，其中包括的情况下，时间序列设计的方法发展和例子，说明其在生物医学研究的各个研究领域的应用。

Biomedical research has been transformed by the recent development of big data technologies. For instance, the collection of health records in linked electronic databases provide information on demographics, health events, medications, and lifestyle factor for large samples of patients. Similarly, portable devices such as mobile phones provide the possibility to recruit large number of participants, and to collect real-time and geo-located individual-level measurements. While these resources offer the possibility to answer research questions that could not be feasibly addressed using traditional studies, they require innovative analytical approaches.This project contributes to address this issue through the development of a novel analytical method called case time series design. This design combines features of existing approaches to generate a more adaptable tool, particularly well-suited for the analysis of highly-informative big data resources. The case time series design is applicable in different research areas for investigating health effects associated with various risk factors, such as environmental exposures, clinical conditions, or drug use. The research proposal is structured in a detailed work plan that includes the methodological development of the case time series design and examples that illustrate its application in various research areas within biomedical research.